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31 32 </head> 33 34 <body class="homepage"> 35 <div class="navbar fixed-top navbar-expand-lg navbar-dark bg-primary"> 36 <div class="container"> 37 <a class="navbar-brand" href=".">Gravitational Waves Data Analysis | Machine Learning</a> 38 <!-- Expander button --> 39 <button type="button" class="navbar-toggler" data-toggle="collapse" data-target="#navbar-collapse"> 40 <span class="navbar-toggler-icon"></span> 41 </button> 42 43 <!-- Expanded navigation --> 44 <div id="navbar-collapse" class="navbar-collapse collapse"> 45 46 <ul class="nav navbar-nav ml-auto"> 47 <li class="nav-item"> 48 <a href="#" class="nav-link" data-toggle="modal" data-target="#mkdocs_search_modal"> 49 <i class="fa fa-search"></i> Search 50 </a> 51 </li> 52 <li class="nav-item"> 53 <a href="https://github.com/iphysresearch/Survey4GWML/edit/master/md/index.md" class="nav-link"><i class="fa fa-github"></i> Edit on GitHub</a> 54 </li> 55 </ul> 56 </div> 57 </div> 58 </div> 59 60 <div class="container"> 61 <div class="row"> 62 <div class="col-md-3"><div class="navbar-light navbar-expand-md bs-sidebar hidden-print affix" role="complementary"> 63 <div class="navbar-header"> 64 <button type="button" class="navbar-toggler collapsed" data-toggle="collapse" data-target="#toc-collapse" title="Table of Contents"> 65 <span class="fa fa-angle-down"></span> 66 </button> 67 </div> 68 69 70 <div id="toc-collapse" class="navbar-collapse collapse card bg-secondary"> 71 <ul class="nav flex-column"> 72 73 <li class="nav-item" data-level="1"><a href="#1_conferences_workshops" class="nav-link">1. Conferences & Workshops</a> 74 <ul class="nav flex-column"> 75 </ul> 76 </li> 77 78 <li class="nav-item" data-level="1"><a href="#2_general_reports_reviews" class="nav-link">2. General Reports & Reviews</a> 79 <ul class="nav flex-column"> 80 </ul> 81 </li> 82 83 <li class="nav-item" data-level="1"><a href="#3_improving_data_quality" class="nav-link">3. Improving Data Quality</a> 84 <ul class="nav flex-column"> 85 <li class="nav-item" data-level="2"><a href="#glitch_classification" class="nav-link">Glitch Classification</a> 86 <ul class="nav flex-column"> 87 </ul> 88 </li> 89 <li class="nav-item" data-level="2"><a href="#glitch_cancellation_gw_denosing" class="nav-link">Glitch cancellation / GW denosing</a> 90 <ul class="nav flex-column"> 91 </ul> 92 </li> 93 </ul> 94 </li> 95 96 <li class="nav-item" data-level="1"><a href="#4_compact_binary_coalesces_cbc" class="nav-link">4. Compact Binary Coalesces (CBC)</a> 97 <ul class="nav flex-column"> 98 <li class="nav-item" data-level="2"><a href="#waveform_modelling" class="nav-link">Waveform Modelling</a> 99 <ul class="nav flex-column"> 100 </ul> 101 </li> 102 <li class="nav-item" data-level="2"><a href="#signal_detection_bbhs" class="nav-link">Signal Detection (BBHs)</a> 103 <ul class="nav flex-column"> 104 </ul> 105 </li> 106 <li class="nav-item" data-level="2"><a href="#parameter_estimation_pe" class="nav-link">Parameter Estimation (PE)</a> 107 <ul class="nav flex-column"> 108 </ul> 109 </li> 110 <li class="nav-item" data-level="2"><a href="#population_studies" class="nav-link">Population Studies</a> 111 <ul class="nav flex-column"> 112 </ul> 113 </li> 114 </ul> 115 </li> 116 117 <li class="nav-item" data-level="1"><a href="#5_continuous_wave_search" class="nav-link">5. Continuous Wave Search</a> 118 <ul class="nav flex-column"> 119 </ul> 120 </li> 121 122 <li class="nav-item" data-level="1"><a href="#6_gravitational_wave_bursts" class="nav-link">6. Gravitational Wave Bursts</a> 123 <ul class="nav flex-column"> 124 </ul> 125 </li> 126 127 <li class="nav-item" data-level="1"><a href="#7_stochastic_gravitational_wave_background" class="nav-link">
1277. Stochastic Gravitational Wave Background</a> 128 <ul class="nav flex-column"> 129 </ul> 130 </li> 131 132 <li class="nav-item" data-level="1"><a href="#8_gw_cosmology" class="nav-link">8. GW / Cosmology</a> 133 <ul class="nav flex-column"> 134 </ul> 135 </li> 136 137 <li class="nav-item" data-level="1"><a href="#9_physics_related" class="nav-link">9. Physics related</a> 138 <ul class="nav flex-column"> 139 </ul> 140 </li> 141 142 <li class="nav-item" data-level="1"><a href="#license" class="nav-link">License</a> 143 <ul class="nav flex-column"> 144 </ul> 145 </li> 146 </ul> 147 </div> 148</div></div> 149 <div class="col-md-9" role="main"> 150 151<blockquote> 152<p><h2>Gravitational Wave Data Analysis with Machine Learning</h2></p> 153<p>This page will give an overview of some problems in gravitational wave data analysis and how researchers are trying to solve them with machine learning. It will include improving data quality, searches for binary black holes and unmodelled gravitational wave bursts, and the astrophysics of gravitational wave sources. I do not include every study in these areas but will do my best. The list can also be found in a web-based <a href="https://www.zotero.org/groups/4595690/survey4gwml">Zotero group</a>.</p> 154</blockquote> 155<ul> 156<li><a href="https://wiki.ligo.org/MLA/WebHome">MLA Web</a> (private access)</li> 157<li><a href="https://www.kaggle.com/c/g2net-gravitational-wave-detection/">G2Net Gravitational Wave Detection</a> (Kaggle data science competation)</li> 158<li><a href="https://www.kaggle.com/competitions/g2net-detecting-continuous-gravitational-waves">G2Net Detecting Continuous Gravitational Waves</a> (Kaggle data science competation)</li> 159<li><a href="https://mathinstitutes.org/videos">NSF - Mathematical Sciences Institutes (video)</a></li> 160<li><a href="https://fastmachinelearning.org">FastML Lab - Real-time and accelerated ML for fundamental sciences</a></li> 161<li><a href="https://a3d3.ai/">Accelerated AI Algorithms for Data-Driven Discovery (A3D3)</a> (<a href="https://www.youtube.com/@nsfhdra3d3/videos">Youtube channel</a>)</li> 162<li><a href="https://www.codabench.org/competitions/2626">NSF HDR A3D3: Detecting Anomalous Gravitational Wave Signals</a> (Codabench competition)</li> 163</ul> 164<hr /> 165<h1 id="1_conferences_workshops">1. Conferences & Workshops<a class="headerlink" href="#1_conferences_workshops" title="Permanent link">¶</a></h1> 166<ul> 167<li>The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) - Events - (<a href="https://iaifi.org/events">Home</a>)</li> 168<li>The IST seminar series Mathematics, Physics & Machine Learning - <span><span class="MathJax_Preview">(M \cup \Phi) \cap M L</span>
168<script type="math/tex">(M \cup \Phi) \cap M L</script>
168</span> - (<a href="https://mpml.tecnico.ulisboa.pt">Home</a>)</li> 169<li>Physics Meets ML - <span><span class="MathJax_Preview">\text{Physics} \cap \text{ML}</span>
169<script type="math/tex">\text{Physics} \cap \text{ML}</script>
169</span> - (<a href="http://www.physicsmeetsml.org/">Home</a>)</li> 170<li>Community Laboratory for AI Research at the Intersection with Physics (CLARIPHY) - Topical Meetings - (<a href="https://clariphy.org/topical.html">Home</a>)</li> 171<li> 172<p>Laboratory for Artificial Intelligence for Scientific Discovery - (<a href="https://ai4science-amsterdam.github.io/">Home</a>)</p> 173</li> 174<li> 175<p>(Dec 8, 2017) - <a href="https://ml4physicalsciences.github.io/2017/">Deep Learning for Physical Sciences</a> (workshop at NeurIPS)</p> 176</li> 177<li>(Oct 17, 2018) - <a href="http://www.ncsa.illinois.edu/Conferences/DeepLearningLSST/">Deep Learning for Multimessenger Astrophysics: Real-time Discovery at Scale</a></li> 178<li>(Sep 10, 2019) - <a href="https://indico.cern.ch/event/822126/">Fast Machine Learning</a> Workshop at Fermi National Accelerator Laboratory</li> 179<li>(Dec 14, 2019) - <a href="https://ml4physicalsciences.github.io/2019">Machine Learning and the Physical Sciences</a> (workshop at NeurIPS)</li> 180<li>(March 9, 2020) - <a href="https://github.com/zerafachris/g2net_2nd_training_school_malta_mar_2020">CA17137</a> - A network for Gravitational Waves, Geophysics and Machine Learning - 2nd Training School (<a href="https://www.g2net.eu">G2NET</a>)</li> 181<li>(April 21, 2020) - Machine Learning for Physicists <a href="https://pad.gwdg.de/s/HJtiTE__U">2020</a></li> 182<li>(19 Oct, 2020) - <a href="http://www.ncsa.illinois.edu/Conferences/AcceleratedAINCSA/">2020 Accelerated Artificial Intelligence for Big-Data Experiments Conference</a></li> 183<li>(Sep 9, 2020) - <a href="https://icerm.brown.edu/programs/sp-f20">Advances in Computational Relativity</a></li> 184<li>(Oct 19, 2020) - <a href="http://www.ncsa.illinois.edu/Conferences/AcceleratedAINCSA/">2020 Accelerated Artificial Intelligence for Big-Data Experiments Conference</a></li> 185<li>(Nov 16, 2020) - <a href="https://icerm.brown.edu/programs/sp-f20/w4/">Statistical Methods for the Detection, Classification, and Inference of Relativistic Objects</a></li> 186<li>(Dec 12, 2020) - <a href="https://inductive-biases.github.io/">Interpretable Inductive Biases and Physically Structured Learning</a> (workshop at NeurIPS)</li> 187<li>(May 27, 2020) - Ellis Fellows Program Quantum and Physics based Machine Learning (QPhML) (<a href="https://ellisqphml.github.io/ellisphilab2021">2021</a>, <a href="https://ellisqphml.github.io/qphml2020">2020</a>, <a href="http://dalimeeting.org/dali2019b/workshop-05-04.html">2019</a>)</li> 188<li>(Dec 13, 2021) - Machine Learning and the Physical Sciences (<a href="https://ml4physicalsciences.github.io/2021/">2021</a>, <a href="https://ml4physicalsciences.github.io/2020/">2020</a>) (workshop at NeurIPS)</li> 189<li>(Dec 6, 2021) - <a href="https://ai4sciencecommunity.github.io/neurips21.html">AI for Science: Mind the Gaps</a> (NeurIPS 2021)</li> 190<li>(Nov 14, 2021) - Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy: From Mathematical Modelling to Machine Learning (<a href="http://www.birs.ca/events/2021/5-day-workshops/21w5066">21w5066</a>)</li> 191<li>(Nov 29, 2021) - <a href="http://www.ipam.ucla.edu/programs/workshops/workshop-iv-big-data-in-multi-messenger-astrophysics/">Workshop IV: Big Data in Multi-Messenger Astrophysics</a> (Part of the Long Program <a href="http://www.ipam.ucla.edu/programs/long-programs/mathematical-and-computational-challenges-in-the-era-of-gravitational-wave-astronomy/">Mathematical and Computational Challenges in the Era of Gravitational Wave Astronomy</a>)</li> 192<li>(July 23, 2022) - <a href="https://ai4sciencecommunity.github.io/icml22.html">AI for Science: Theories and Foundations</a> (ICML 2022)</li> 193<li>(July 22, 2022) - <a href="https://ml4astro.github.io/icml2022/">Machine Learning for Astrophysics</a> (ICML 2022)</li> 194<li>(August 1-9, 2022) - <a href="https://iaifi.org/phd-summer-school.html">IAIFI Summer School & Workshop, 2022</a> | <a href="https://iphysresearch.github.io/blog/post/dl_notes/iaifi/">Recap</a></li> 195<li>(September 28-30, 2022) - <a href="https://indico.ego-gw.it/event/464/">Machine Learning in GW search: g2net next challenges</a></li> 196<li>(Nov 21-25, 2022) - <a href="https://indico.in2p3.fr/event/27706/timetable/?view=standard">LISA data analysis: from classical methods to machine learning</a></li> 197<li>(Dec 2, 2022) - <a href="https://ai4sciencecommunity.github.io/neurips22.html">AI for Science: Progress and Promises</a> (NeurIPS 2022)</li> 198<li>(January 28, 2023) - <a href="https://indico.cern.ch/event/1224718/">Accelerating Physics with ML@MIT</a></li> 199<li>(March 28-30, 2023) - <a href="https://indico.physics.auth.gr/event/14/">G2Net - A network for Gravitational Waves, Geophysics and Machine Learning (CA17137)</a></li> 200<li>(July 28, 2023) - <a href="https://ml4astro.github.io/icml2023/">Machine Learning for Astrophysics</a> (ICML 2023)</li> 201<li>(May 22, 2023) - <a href="https://indico.flatironinstitute.org/event/3624/">Cosmic Connections: A ML X Astrophysics Symposium at Simons Foundation</a></li> 202<li>(Dec 15, 2023) - <a href="https://ai4sciencecommunity.github.io/neurips23.html">AI for Scientific Discovery: From Theory to Practice</a> (NeurIPS 2023)</li> 203<li>(April 23, 2024) - <a href="https://www.gla.ac.uk/events/conferences/aislands-arran24/">The 2024 AIslands meeting</a> focusing on artificial intelligence in gravitational wave astronomy.</li> 204<li>(June 17, 2024) - <a href="https://astroai.cfa.harvard.edu/">AstroAI Workshop - Unveiling the Universe with AI/ML</a></li> 205<li>(Nov 17, 2024) - <a href="https://www.birs.ca/events/2024/5-day-workshops/24w5177">Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy (24w5177)</a></li> 206<li>(June 2, 2025) - <a href="https://icerm.brown.edu/program/topical_workshop/tw-25-smlgwa">Scientific Machine Learning for Gravitational Wave Astronomy (ICERM)</a></li> 207</ul> 208<hr /> 209<h1 id="2_general_reports_reviews">2. General Reports & Reviews<a class="headerlink" href="#2_general_reports_reviews" title="Permanent link">¶</a></h1> 210<blockquote> 211<p>Modern deep learning methods have entered the field of physics which can be tasked with <strong>learning physics from raw data when no good mathematical models are available</strong>. They are also part of mathematical model and machine learning hybrids, formed to reduce computational costs by having the mathematical model train a machine learning model to perform its job, or to improve the fit with observations in settings where the mathematical model canât incorporate all details (think noise).</p> 212</blockquote> 213<ul> 214<li><strong>[Wong (2023)]</strong> - MACHINE LEARNING-ENHANCED ANALYSIS IN GW (<a href="https://kazewong.github.io/MyUnhingedPresnetations/slides/scma8/top.html">slide</a>)</li> 215<li><strong>[Agarwal et al. (2023) <sup id="fnref:1"><a class="footnote-ref" href="#fn:1">1</a></sup> (2306.08106)]</strong> - Applications of Deep Learning to Physics Workflows</li> 216<li><strong>[Huerta et al. (2022) <sup id="fnref:2"><a class="footnote-ref" href="#fn:2">2</a></sup> (Scientific Data)]</strong> - FAIR for AI: An Interdisciplinary, International, Inclusive, and Diverse Community Building Perspective</li> 217<li><strong>[Cuoco et al. (2022) <sup id="fnref:3"><a class="footnote-ref" href="#fn:3">3</a></sup> (Nature Computational Science)]</strong> - Computational Challenges for Multimodal Astrophysics</li> 218<li><strong>[Ravi et al. (2022) <sup id="fnref:4"><a class="footnote-ref" href="#fn:4">4</a></sup> (Scientific Data)]</strong> - FAIR Principles for AI Models, with a Practical Application for Accelerated High Energy Diffraction Microscopy</li> 219<li><strong>[Gunny et al. (2022) [@10.1145/3526058.3535454] (ACM)]</strong> - A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics</li> 220<li><strong>[Harris et al. (2022) <sup id="fnref:5"><a class="footnote-ref" href="#fn:5">5</a></sup> (2203.16255)]</strong> - Physics Community Needs, Tools, and Resources for Machine Learning</li> 221<li><strong>[Deiana et al. (2022) <sup id="fnref:6"><a class="footnote-ref" href="#fn:6">6</a></sup> ( Front. big data)]</strong> - Applications and Techniques for Fast Machine Learning in Science</li> 222<li><strong>[Dvorkin et al. (2022) <sup id="fnref:7"><a class="footnote-ref" href="#fn:7">7</a></sup> (2203.08056)]</strong> - Machine Learning and Cosmology</li> 223<li><strong>[Smith (2021) <sup id="fnref:8"><a class="footnote-ref" href="#fn:8">8</a></sup> (Nature Physics)]</strong> - OK Computer</li> 224<li><strong>[Ntampaka et al. (2021) <sup id="fnref:9"><a class="footnote-ref" href="#fn:9">9</a></sup> (2111.14566)]</strong> - Building Trustworthy Machine Learning Models for Astronomy</li> 225<li><strong>[Huerta & Zhao (2021) <sup id="fnref:10"><a class="footnote-ref" href="#fn:10">10</a></sup> (Handbook of Gravitational Wave Astronomy)]</strong> - Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-messenger Sources</li> 226<li><strong>[Agrawal et al. (2020) <sup id="fnref:11"><a class="footnote-ref" href="#fn:11">11</a></sup> (Springer Singapore)]</strong> - Machine Learning Based Analysis of Gravitational Waves</li> 227<li><strong>[Huerta et al. (2021) <sup id="fnref:12"><a class="footnote-ref" href="#fn:12">12</a></sup> (Nature Astronomy)]</strong> - Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection</li> 228<li><strong>[Coughlin (2020) <sup id="fnref:13"><a class="footnote-ref" href="#fn:13">13</a></sup> (Nature Astronomy)]</strong> - Lessons from Counterpart Searches in LIGO and Virgo’s Third Observing Campaign</li> 229<li><strong>[Zdeborová (2020) <sup id="fnref:14"><a class="footnote-ref" href="#fn:14">14</a></sup> (Nature Physics)]</strong> - Understanding Deep Learning Is Also a Job for Physicists</li> 230<li><strong>[Cuoco et al. (2020) <sup id="fnref:15"><a class="footnote-ref" href="#fn:15">
23015</a></sup> (Mach. learn.: sci. technol.)]</strong> - Enhancing Gravitational-wave Science with Machine Learning</li> 231<li><strong>[Huerta et al. (2020) <sup id="fnref:16"><a class="footnote-ref" href="#fn:16">16</a></sup> (J. Big Data)]</strong> - Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure</li> 232<li><strong>[Fluke et al. (2020) <sup id="fnref:17"><a class="footnote-ref" href="#fn:17">17</a></sup> (WIRDMKD)]</strong> - Surveying the reach and maturity of machine learning and artificial intelligence in astronomy</li> 233<li><a href="https://physics.aps.org/articles/v13/40">Viewpoint: Machine Learning Tackles Spacetime</a> By <a href="https://physics.aps.org/authors/enrico_rinaldi">Enrico Rinaldi</a> March 23, 2020 - Physics 13, 40</li> 234<li><strong>[Huerta et al. (2019) <sup id="fnref:18"><a class="footnote-ref" href="#fn:18">18</a></sup> (CSBS)]</strong> - Supporting High-Performance and High-Throughput Computing for Experimental Science</li> 235<li><strong>[Huerta et al. (2019) <sup id="fnref:19"><a class="footnote-ref" href="#fn:19">19</a></sup> (Nature Reviews Physics)]</strong> - Enabling Real-Time Multi-Messenger Astrophysics Discoveries with Deep Learning</li> 236<li><strong>[Allen et al. (2019) <sup id="fnref:20"><a class="footnote-ref" href="#fn:20">20</a></sup> (1902.00522)]</strong> - Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era</li> 237<li><strong>[Eadie et al. (2019) <sup id="fnref:21"><a class="footnote-ref" href="#fn:21">21</a></sup> (1909.11714)]</strong> - Realizing the Potential of Astrostatistics and Astroinformatics</li> 238<li><strong>[Foley et al. (2019) <sup id="fnref:22"><a class="footnote-ref" href="#fn:22">22</a></sup> (1903.04553)]</strong> - Gravity and Light-Combining Gravitational Wave and Electromagnetic Observations in the 2020s</li> 239</ul> 240<hr /> 241<h1 id="3_improving_data_quality">3. Improving Data Quality<a class="headerlink" href="#3_improving_data_quality" title="Permanent link">¶</a></h1> 242<p>Machine learning techniques have proved to be powerful tools in analyzing complex problems by learning from large example datasets. They have been applied in GW science from as early as <strong>[Lightman et al. (2006) <sup id="fnref:23"><a class="footnote-ref" href="#fn:23">23</a></sup> (JPCS)]</strong> to the study of glitches <strong>[Essick et al. (2013) <sup id="fnref:24"><a class="footnote-ref" href="#fn:24">24</a></sup> (CQG); Biswas et al. (2013) <sup id="fnref:25"><a class="footnote-ref" href="#fn:25">25</a></sup> (PRD)]</strong> and other problems, such as signal characterization <strong>[Baker et al. (2015) <sup id="fnref:26"><a class="footnote-ref" href="#fn:26">26</a></sup> (PRD)]</strong> . For example, Gstlal-iDQ <strong>[Vaulin et al. (2013) <sup id="fnref:27"><a class="footnote-ref" href="#fn:27">27</a></sup>]</strong> (a streaming machine learning pipeline based on <strong>[Essick et al. (2013) <sup id="fnref2:24"><a class="footnote-ref" href="#fn:24">24</a></sup> (CQG)]</strong> and <strong>[Biswas et al. (2013) <sup id="fnref2:25"><a class="footnote-ref" href="#fn:25">25</a></sup> (PRD)]</strong> reported the probability that there was a glitch in <span><span class="MathJax_Preview">h(t)</span>
242<script type="math/tex">h(t)</script>
242</span> based on the presence of glitches in witness sensors at the time of the event. In O2, iDQ was used to vet unmodeled low-latency pipeline triggers automatically. </p> 243<h2 id="glitch_classification">Glitch Classification<a class="headerlink" href="#glitch_classification" title="Permanent link">¶</a></h2> 244<p>Some glitches occur only in the GW data channel. We can try and eliminate them by classifying them into different types to help identify their origin. Unfortunately, there is a number of identified classes of glitches for which mitigation methods are not yet understood. For these glitch classes, understanding how searches can separate instrumental transients from similar astrophysical signals is the highest priority <strong>[Davis et al. (2020) <sup id="fnref:28"><a class="footnote-ref" href="#fn:28">28</a></sup> (CQG)]</strong>.</p> 245<ul> 246<li> 247<p>PCA based</p> 248<ul> 249<li> 250<p>Early ML studies for glitch classification used Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM). (See <strong>[Powell et al. (2015) <sup id="fnref:29"><a class="footnote-ref" href="#fn:29">29</a></sup> (CQG)]</strong> test on simulated data & <strong>[Powell et al. (2017) <sup id="fnref:30"><a class="footnote-ref" href="#fn:30">30</a></sup> (CQG)]</strong> test on real data). A trigger generator finds the glitches. The time series of whitened glitches are stored in a matrix D on which PCA is performed. See more on <strong>[Powell (2017) <sup id="fnref:31"><a class="footnote-ref" href="#fn:31">31</a></sup> (PhD Thesis); Cuoco (2018) <sup id="fnref:32"><a class="footnote-ref" href="#fn:32">32</a></sup> (Workshop)]</strong></p> 251</li> 252<li> 253<p>PCA is an orthogonal linear transformation that transforms a set of correlated variables into another set of linearly uncorrelated variables, called Principal Components (PCs). The matrix <span><span class="MathJax_Preview">D</span>
253<script type="math/tex">D</script>
253</span> is factored so that <span><span class="MathJax_Preview">D= U\Sigma V^T</span>
253<script type="math/tex">D= U\Sigma V^T</script>
253</span> where <span><span class="MathJax_Preview">V=A^TA</span>
253<script type="math/tex">V=A^TA</script>
253</span>, <span><span class="MathJax_Preview">\Sigma</span>
253<script type="math/tex">\Sigma</script>
253</span> contains eigenvalues, and <span><span class="MathJax_Preview">U</span>
253<script type="math/tex">U</script>
253</span> is the PCs. PC coefficients are calculated by taking the dot product of the PCs and the whitened glitch. Then GMM clustering is applied to the PC coefficients. These studies were then improved with the use of Neural Networks.</p> 254<blockquote></blockquote> 255</li> 256</ul> 257</li> 258<li> 259<p>CNN (Images feature)</p> 260<ul> 261<li><strong>[Razzano & Cuoco 2018 <sup id="fnref:33"><a class="footnote-ref" href="#fn:33">33</a></sup> (CQG)]</strong> apply a CNN to simulated glitches. They build images that cover 2 seconds around each glitch from the whitened time series. Simulated six families of signals. Training, validation, and test set with ratio 70:15:15. Accuracy in the order of â98-99% on multiclass classification </li> 262<li><strong>[George et al. (2018) <sup id="fnref2:34"><a class="footnote-ref" href="#fn:34">34</a></sup> (PRD)]</strong>: âDeep learning techniques are a promising tool for the recognition and classification of glitches. We present a classification pipeline that exploits Convolutional Neural Networks to classify glitches starting from their time frequency evolution represented as images. We evaluated the classification accuracy on simulated glitches, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization.â <blockquote></blockquote> 263</li> 264</ul> 265</li> 266<li> 267<p>Wavelet-based (Time series feature)</p> 268<ul> 269<li><strong>[Cuoco et al. (2018) <sup id="fnref:35"><a class="footnote-ref" href="#fn:35">35</a></sup> (IEEE)]</strong> - Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors<blockquote></blockquote> 270</li> 271</ul> 272</li> 273<li> 274<p>GravitySpy Project</p> 275<ul> 276<li>GravitySpy <strong>[Zevin et al. (2017) <sup id="fnref:36"><a class="footnote-ref" href="#fn:36">36</a></sup> (CQG); Coughlin et al. (2019) <sup id="fnref:37"><a class="footnote-ref" href="#fn:37">37</a></sup> (PRD)]</strong> uses citizen scientists to produce training sets for machine learning glitch classification.</li> 277<li>(How do I try it myself?) Log into <a href="https://www.gravityspy.org">gravityspy.org</a> to try classifying glitches. Download already labelled LIGO glitches for training your algorithm from zenodo <a href="https://zenodo.org/record/1476156">One</a> / <a href="https://zenodo.org/record/1476551">Two</a>.</li> 278<li><strong>[Soni et al. (2021) <sup id="fnref:38"><a class="footnote-ref" href="#fn:38">38</a></sup> (CQG)]</strong> - Discovering Features in Gravitational-wave Data through Detector Characterization, Citizen Science and Machine Learning<blockquote></blockquote> 279</li> 280</ul> 281</li> 282<li> 283<p>Others / Pending:</p> 284<ul> 285<li><strong>[Staats & Cavaglià (2018) <sup id="fnref:39"><a class="footnote-ref" href="#fn:39">39</a></sup> (Commun. Comput. Phys.)]</strong> - Finding the origin of noise transients in LIGO data with machine learning (<strong>Karoo GP</strong>)</li> 286<li><strong>[Mukund et al. (2017) <sup id="fnref:40"><a class="footnote-ref" href="#fn:40">40</a></sup> (PRD)]</strong> - Transient classification in LIGO data using difference boosting neural network (<strong>Wavelet-DBNN, India</strong>)</li> 287<li><strong>[Llorens-Monteagudo et al. (2019) <sup id="fnref:41"><a class="footnote-ref" href="#fn:41">41</a></sup> (CQG)]</strong> - Classification of gravitational-wave glitches via dictionary learning (<strong>Dictionary learning</strong>)</li> 288<li>Low latency transient detection and classification (I. Pinto, V. Pierro, L. Troiano, E. Mejuto-Villa, V. Matta, P. Addesso)</li> 289<li><strong>[George et al. (2018) <sup id="fnref:34"><a class="footnote-ref" href="#fn:34">34</a></sup> (PRD)]</strong> - Classification and unsupervised clustering of LIGO data with Deep Transfer Learning (<strong>Deep Transfer Learning</strong>)</li> 290<li><strong>[Astone et al. (2018) <sup id="fnref2:42"><a class="footnote-ref" href="#fn:42">42</a></sup> (PRD)]</strong> - New method to observe gravitational waves emitted by core collapse supernovae (<strong>RGB image SN CNN</strong>)</li> 291<li><strong>[Colgan et al. (2020) <sup id="fnref:43"><a class="footnote-ref" href="#fn:43">43</a></sup> (PRD)]</strong> - Efficient gravitational-wave glitch identification from environmental data through machine learning</li> 292<li><strong>[Bahaadini et al. (2017) <sup id="fnref:44"><a class="footnote-ref" href="#fn:44">44</a></sup> (IEEE)]</strong> - Deep Multi-View Models for Glitch Classification</li> 293<li><strong>[Bahaadini et al. (2018) <sup id="fnref:45"><a class="footnote-ref" href="#fn:45">45</a></sup> (Info. Sci.)]</strong> - Machine learning for Gravity Spy: Glitch classification and dataset</li> 294<li><strong>[Bahaadini et al. (2018) <sup id="fnref:46"><a class="footnote-ref" href="#fn:46">46</a></sup> (IEEE)]</strong> - DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data</li> 295<li>Young-Min Kim - Noise Identification in Gravitational wave search using Artificial Neural Networks (<a href="https://gwdoc.icrr.u-tokyo.ac.jp/DocDB/0017/G1301718/003/KJ-KAGRA_20130610_YMKIM.pdf">PDF</a>) (4th K-J workshop on KAGRA @ Osaka Univ.)</li> 296<li><strong>[Biswas et al. (2020) <sup id="fnref:47"><a class="footnote-ref" href="#fn:47">47</a></sup> (CQG)]</strong> - New Methods to Assess and Improve LIGO Detector Duty Cycle</li> 297<li><strong>[Morales-Alvarez et al. (2020) <sup id="fnref:48"><a class="footnote-ref" href="#fn:48">48</a></sup> (IEEE)]</strong> - Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO</li> 298<li><strong>[Marianer et al. (2020) <sup id="fnref2:49"><a class="footnote-ref" href="#fn:49">49</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - A Semisupervised Machine Learning Search for Never-seen Gravitational-wave Sources</li> 299<li><strong>[Mesuga & Bayanay (2021) <sup id="fnref:50"><a class="footnote-ref" href="#fn:50">50</a></sup> (2107.01863)]</strong> - On the Efficiency of Various Deep Transfer Learning Models in Glitch Waveform Detection in Gravitational-wave Data</li> 300<li><strong>[Sankarapandian & Kulis (2021) <sup id="fnref:51"><a class="footnote-ref" href="#fn:51">51</a></sup> (2107.10667)]</strong> - <span><span class="MathJax_Preview">β</span>
300<script type="math/tex">β</script>
300</span>-Annealed Variational Autoencoder for Glitches</li> 301<li><strong>[Yu & Adhikari (2021) <sup id="fnref:52"><a class="footnote-ref" href="#fn:52">52</a></sup> (2111.03295)]</strong> - Nonlinear Noise Regression in Gravitational-Wave Detectors with Convolutional Neural Networks</li> 302<li><strong>[Sakai et al. (2021) <sup id="fnref:53"><a class="footnote-ref" href="#fn:53">53</a></sup> (2111.10053)]</strong> - Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors</li> 303<li><strong>[Merritt et al. (2021) <sup id="fnref:54"><a class="footnote-ref" href="#fn:54">54</a></sup> (PRD)]</strong> - Transient Glitch Mitigation in Advanced LIGO Data</li> 304<li><strong>[Colgan et al. (2022) <sup id="fnref:55"><a class="footnote-ref" href="#fn:55">55</a></sup> (2202.13486)]</strong> - Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets</li> 305<li><strong>[Davis et al. (2022) <sup id="fnref:56"><a class="footnote-ref" href="#fn:56">56</a></sup> (2204.03091)]</strong> - Incorporating Information from LIGO Data Quality Streams into the PyCBC Search for Gravitational Waves</li> 306<li><strong>[Bahaadini et al. (2022) <sup id="fnref:57"><a class="footnote-ref" href="#fn:57">57</a></sup> (2205.13672)]</strong> - Discriminative Dimensionality Reduction Using Deep Neural Networks for Clustering of LIGO Data</li> 307<li><strong>[Yan et al. (2022) <sup id="fnref:58"><a class="footnote-ref" href="#fn:58">58</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - On Improving the Performance of Glitch Classification for Gravitational Wave Detection by Using Generative Adversarial Networks</li> 308<li><strong>[Sakai et al. (2022) <sup id="fnref:59"><a class="footnote-ref" href="#fn:59">59</a></sup> (2208.03623)]</strong> - Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset</li> 309<li><strong>[Powell et al. (2022) <sup id="fnref:60"><a class="footnote-ref" href="#fn:60">60</a></sup> (CQG)]</strong> Generating Transient Noise Artifacts in Gravitational-Wave Detector Data with Generative Adversarial Networks</li> 310<li><strong>[Iqbal (2022) <sup id="fnref:61"><a class="footnote-ref" href="#fn:61">61</a></sup> (ResearchGate)]</strong> - Classifying the Gravitational Waves Using the Deep Learning Technique</li> 311<li><strong>[Ferreira et al. (2022) <sup id="fnref:62"><a class="footnote-ref" href="#fn:62">62</a></sup> (CQG)]</strong> - Comparison between T-SNE and Cosine Similarity for LIGO Glitches Analysis</li> 312<li><strong>[Dooney et al. (2022) <sup id="fnref:63"><a class="footnote-ref" href="#fn:63">63</a></sup> (2209.13592)]</strong> - DVGAN: Stabilize Wasserstein GAN Training for Time-Domain Gravitational Wave Physics</li> 313<li><strong>[Boudart et al. (2022) <sup id="fnref:64"><a class="footnote-ref" href="#fn:64">64</a></sup> (2210.04588)]</strong> - A Convolutional Neural Network to Distinguish Glitches from Minute-Long Gravitational Wave Transients</li> 314<li><strong>[BiÅkin et al. (2022) <sup id="fnref:65"><a class="footnote-ref" href="#fn:65">65</a></sup> (Astron. Comput.)]</strong> - A Fast and Time-Efficient Glitch Classification Method: A Deep Learning-Based Visual Feature Extractor for Machine Learning Algorithms</li> 315<li><strong>[Ruiz et al. (2022) <sup id="fnref:66"><a class="footnote-ref" href="#fn:66">66</a></sup> (KBS)]</strong> - Probabilistic Fusion of Crowds and Experts for the Search of Gravitational Waves</li> 316<li><strong>[Razzano et al. (2022) <sup id="fnref:67"><a class="footnote-ref" href="#fn:67">67</a></sup> (Nucl. Instrum.)]</strong> - GWitchHunters: Machine Learning and Citizen Science to Improve the Performance of Gravitational Wave Detector</li> 317<li><strong>[Bini et al. (2023) <sup id="fnref:68"><a class="footnote-ref" href="#fn:68">68</a></sup> (2303.05986)]</strong> - An Autoencoder Neural Network Integrated into Gravitational-Wave Burst Searches to Improve the Rejection of Noise Transients</li> 318<li><strong>[Fernandes et al. (2023) <sup id="fnref:69"><a class="footnote-ref" href="#fn:69">69</a></sup> (2303.13917)]</strong> - Convolutional Neural Networks for the Classification of Glitches in Gravitational-Wave Data Streams</li> 319<li><strong>[Alvarez-Lopez et al. (2023) [@2023Alvarez-LopezGSpyNetTreesignalvsglitchclassifier] (2304.09977)]</strong> - GSpyNetTree: A Signal-vs-Glitch Classifier for Gravitational-Wave Event Candidates</li> 320<li><strong>[Shah et al. (2023) <sup id="fnref:70"><a class="footnote-ref" href="#fn:70">70</a></sup> (2306.13787)]</strong> - Waves in a Forest: A Random Forest Classifier to Distinguish between Gravitational Waves and Detector Glitches</li> 321<li><strong>[Raza et al. (2023) <sup id="fnref:71"><a class="footnote-ref" href="#fn:71">
32171</a></sup> (2308.12357)]</strong> - Explaining the GWSkyNet-Multi Machine Learning Classifier Predictions for Gravitational-Wave Events</li> 322</ul> 323</li> 324</ul> 325<h2 id="glitch_cancellation_gw_denosing">Glitch cancellation / GW denosing<a class="headerlink" href="#glitch_cancellation_gw_denosing" title="Permanent link">¶</a></h2> 326<blockquote></blockquote> 327<ul> 328<li>Pending:<ul> 329<li><strong>[Cuoco et al. (2001) <sup id="fnref:72"><a class="footnote-ref" href="#fn:72">72</a></sup> (CQG)]</strong> - On-line power spectra identification and whitening for the noise in interferometric gravitational wave detectors</li> 330<li><strong>[Torres-Forné (2016) <sup id="fnref:73"><a class="footnote-ref" href="#fn:73">73</a></sup> (PRD)]</strong> - Denoising of Gravitational Wave Signals Via Dictionary Learning Algorithms</li> 331<li><strong>[Torres et al. (2014) [@2014TorresTotalvariationbasedmethodsgravitational] (PRD)]</strong> - Total-Variation-Based Methods for Gravitational Wave Denoising</li> 332<li><strong>[Torres-Forné (2018) [@torres2018total] (PRD)]</strong> - Total-variation methods for gravitational-wave denoising: Performance tests on Advanced LIGO data</li> 333<li><strong>[Torres-Forné (2020) <sup id="fnref:74"><a class="footnote-ref" href="#fn:74">74</a></sup> (PRD)]</strong> - Application of dictionary learning to denoise LIGO’s blip noise transients</li> 334<li><strong>[Shen et al. (2019) [@Shen2019ohi] (IEEE)]</strong> - Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-encoders</li> 335<li><strong>[Wei & Huerta (2020) [@2020WeiGravitationalwavedenoising] (PLB)]</strong> - Gravitational wave denoising of binary black hole mergers with deep learning</li> 336<li><strong>[Vajente et al. (2020) [@Vajente2019ycy] (PRD)]</strong> - Machine-learning nonstationary noise out of gravitational-wave detectors</li> 337<li><strong>[Alimohammadi et al. (2021) <sup id="fnref:75"><a class="footnote-ref" href="#fn:75">75</a></sup> (Scientific Reports)]</strong> - A Template-Free Approach for Waveform Extraction of Gravitational Wave Events</li> 338<li><strong>[Ormiston et al. (2020) <sup id="fnref:76"><a class="footnote-ref" href="#fn:76">76</a></sup> (PRR)]</strong> - Noise Reduction in Gravitational-Wave Data via Deep Learning</li> 339<li><strong>[Essick et al. (2020) <sup id="fnref:77"><a class="footnote-ref" href="#fn:77">77</a></sup> (Mach. learn.: sci. technol.)]</strong> - iDQ: Statistical Inference of Non-gaussian Noise with Auxiliary Degrees of Freedom in Gravitational-wave Detectors</li> 340<li><strong>[Mogushi et al. (2021) [@2021MogushiNNETFIXartificialneural] (Mach. learn.: sci. technol.)]</strong> - NNETFIX: an artificial neural network-based denoising engine for gravitational-wave signals</li> 341<li><strong>[Chatterjee et al. (2021) [@2021ChatterjeeExtractionBinaryBlack] (PRD)]</strong> - Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning</li> 342<li><strong>[Mogushi (2021) <sup id="fnref:78"><a class="footnote-ref" href="#fn:78">78</a></sup> (2105.10522)]</strong> - Reduction of Transient Noise Artifacts in Gravitational-wave Data Using Deep Learning</li> 343<li><strong>[Colgan et al. (2022) <sup id="fnref:79"><a class="footnote-ref" href="#fn:79">79</a></sup> (2203.05086)]</strong> - Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning</li> 344<li><strong>[Lopez et al. (2022) [@2022LopezSimulatingTransientNoise] (2203.06494)]</strong> - Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks</li> 345<li><strong>[Yu & Adhikari (2022) <sup id="fnref:80"><a class="footnote-ref" href="#fn:80">80</a></sup> (Front. Artif. Intell.)]</strong>
345 - Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks</li> 346<li><strong>[Lopez et al. (2022) [@2022LopezSimulatingTransientNoisea] (2205.09204)]</strong> - Simulating Transient Noise Bursts in LIGO with Gengli</li> 347<li><strong>[Vajente (2022) [@PhysRevD.105.102005] (PRD)]</strong> - Data Mining and Machine Learning Improve Gravitational-Wave Detector Sensitivity</li> 348<li><strong>[Bacon et al. (2022) [@2022BaconDenoisinggravitationalwavesignals] (2205.13513)]</strong> - Denoising Gravitational-Wave Signals from Binary Black Holes with Dilated Convolutional Autoencoder</li> 349<li><strong>[Kato et al. (2022) <sup id="fnref:81"><a class="footnote-ref" href="#fn:81">81</a></sup> (Astron. Comput.)]</strong> - Validation of Denoising System Using Non-Harmonic Analysis and Denoising Convolutional Neural Network for Removal of Gaussian Noise from Gravitational Waves Observed by LIGO</li> 350<li><strong>[Ashton (2022) <sup id="fnref:82"><a class="footnote-ref" href="#fn:82">82</a></sup> (2209.15547)]</strong> - Gaussian Processes for Gravitational-wave Astronomy</li> 351<li><strong>[Murali & Lumley (2022) <sup id="fnref:83"><a class="footnote-ref" href="#fn:83">83</a></sup> (2210.01718)]</strong> - Detecting and Denoising Gravitational Wave Signals from Binary Black Holes Using Deep Learning</li> 352<li><strong>[Yang et al. (2023) <sup id="fnref:84"><a class="footnote-ref" href="#fn:84">84</a></sup> (2305.06735)]</strong> - Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on Noise2Noise Method</li> 353<li><strong>[Saleem et al. (2023) [@2023SaleemDemonstrationMachineLearningassisted] (2306.11366)]</strong> - Demonstration of Machine Learning-assisted Real-Time Noise Regression in Gravitational Wave Detectors</li> 354</ul> 355</li> 356</ul> 357<hr /> 358<h1 id="4_compact_binary_coalesces_cbc">4. Compact Binary Coalesces (CBC)<a class="headerlink" href="#4_compact_binary_coalesces_cbc" title="Permanent link">¶</a></h1> 359<h2 id="waveform_modelling">Waveform Modelling<a class="headerlink" href="#waveform_modelling" title="Permanent link">¶</a></h2> 360<p>Signal models are needed for matched filtering and parameter estimation. Solutions of the Einstein equations can be obtained with numerical relativity simulations - High computational cost! LIGO and Virgo rely on approximate solutions obtained through phenomenological modelling. Gaussian process regression has been used to produce new waveforms by providing a direct interpolation between numerical simulations. For Example, <strong>[Docter et al. (2017) <sup id="fnref:85"><a class="footnote-ref" href="#fn:85">85</a></sup> (PRD)]</strong></p> 361<ul> 362<li> 363<p>Machine Learning for waveform generation: Use optimal waveform generators, i.e., machine learning models trained with numerical relativity waveforms <strong>[Blackman et al. (2017) <sup id="fnref:86"><a class="footnote-ref" href="#fn:86">86</a></sup> (PRD); Huerta et al. (2018) <sup id="fnref:87"><a class="footnote-ref" href="#fn:87">87</a></sup> (PRD)]</strong> (For eccentric black hole mergers <strong>[Varma et al. (2019) <sup id="fnref:88"><a class="footnote-ref" href="#fn:88">88</a></sup> (PRD); Varma et al. (2019) <sup id="fnref:89"><a class="footnote-ref" href="#fn:89">89</a></sup> (PRR)]</strong>)</p> 364<blockquote></blockquote> 365</li> 366<li> 367<p>Pending:</p> 368<ul> 369<li><strong>[Chua (2017) <sup id="fnref:90"><a class="footnote-ref" href="#fn:90">90</a></sup> (PhD Thesis)]</strong> - Topics in gravitational-wave astronomy: Theoretical studies, source modelling and statistical methods</li> 370<li><strong>[Chua et al. (2019) <sup id="fnref:91"><a class="footnote-ref" href="#fn:91">91</a></sup> (PRL)]</strong> - Reduced-oruder modeling with artificial neurons for gravitational-wave inference</li> 371<li><strong>[Setyawati et al. (2020) [@2020SetyawatiRegressionmethodswaveform] (CQG)]</strong> - Regression Methods in Waveform Modeling: A Comparative Study</li> 372<li><strong>[Tiglio & Villanueva (2019) [@2021TiglioAbInitiobased] (Scientific Reports)]</strong> - On Ab Initio-based, Free and Closed-form Expressions for Gravitational Waves</li> 373<li><strong>[Schmidt (2019) <sup id="fnref:92"><a class="footnote-ref" href="#fn:92">92</a></sup> (Masters Thesis)]</strong> - Gravitational Wave Modelling with Machine Lerning</li> 374<li><strong>[Chen et al. (2020) <sup id="fnref:93"><a class="footnote-ref" href="#fn:93">93</a></sup>
374 (2008.03313)]</strong> - Observation of Eccentric Binary Black Hole Mergers with Second and Third Generation Gravitational Wave Detector Networks</li> 375<li><strong>[Khan & Green (2020) <sup id="fnref:94"><a class="footnote-ref" href="#fn:94">94</a></sup> (PRD)]</strong> - Gravitational-wave Surrogate Models Powered by Artificial Neural Networks</li> 376<li><strong>[Schmidt et al. (2020) <sup id="fnref:95"><a class="footnote-ref" href="#fn:95">95</a></sup> (PRD)]</strong> - Machine Learning Gravitational Waves from Binary Black Hole Mergers</li> 377<li><strong>[Lee et al. (2021) <sup id="fnref:96"><a class="footnote-ref" href="#fn:96">96</a></sup> (PRD)]</strong> - Deep Learning Model on Gravitational Waveforms in Merging and Ringdown Phases of Binary Black Hole Coalescences</li> 378<li><strong>[Liao & Lin (2021) <sup id="fnref:97"><a class="footnote-ref" href="#fn:97">97</a></sup> (PRD)]</strong> - Deep Generative Models of Gravitational Waveforms via Conditional Autoencoder</li> 379<li><strong>[Chua et al. (2021) <sup id="fnref:98"><a class="footnote-ref" href="#fn:98">98</a></sup> (PRL)]</strong> - Rapid Generation of Fully Relativistic Extreme-mass-ratio-inspiral Waveform Templates for LISA Data Analysis</li> 380<li><strong>[Keith et al. (2021) <sup id="fnref:99"><a class="footnote-ref" href="#fn:99">99</a></sup> (PRR)]</strong> - Orbital Dynamics of Binary Black Hole Systems Can Be Learned from Gravitational Wave Measurements</li> 381<li><strong>[McGinn et al. (2021) <sup id="fnref:100"><a class="footnote-ref" href="#fn:100">100</a></sup> (CQG)]</strong> - Generalised Gravitational Burst Generation with Generative Adversarial Networks</li> 382<li><strong>[Nousi et al. (2021) <sup id="fnref:101"><a class="footnote-ref" href="#fn:101">101</a></sup> (2107.04312)]</strong></li> 383<li><strong>[Barsotti et al. (2021) <sup id="fnref:102"><a class="footnote-ref" href="#fn:102">102</a></sup> (CQG)]</strong> - Gravitational Wave Surrogates through Automated Machine Learning</li> 384<li><strong>[Khan et al. (2022) <sup id="fnref:103"><a class="footnote-ref" href="#fn:103">103</a></sup> (PRD)]</strong> - Interpretable AI Forecasting for Numerical Relativity Waveforms of Quasicircular, Spinning, Nonprecessing Binary Black Hole Mergers</li> 385<li><strong>[Coogan et al. (2022) <sup id="fnref:104"><a class="footnote-ref" href="#fn:104">104</a></sup> (PRD)]</strong> - Efficient Gravitational Wave Template Bank Generation with Differentiable Waveforms</li> 386<li><strong>[Freitas et al. (2022) <sup id="fnref:105"><a class="footnote-ref" href="#fn:105">105</a></sup> (2203.01267)]</strong> - Generating Gravitational Waveform Libraries of Exotic Compact Binaries with Deep Learning</li> 387<li><strong>[Freitas et al. (2022) <sup id="fnref:106"><a class="footnote-ref" href="#fn:106">106</a></sup> (2203.08434)]</strong> - Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling</li> 388<li><strong>[Thomas et al. (2022) <sup id="fnref:107"><a class="footnote-ref" href="#fn:107">107</a></sup> (2205.14066)]</strong> - Accelerating Multimodal Gravitational Waveforms from Precessing Compact Binaries with Artificial Neural Networks</li> 389<li><strong>[Ferguson (2022) <sup id="fnref:108"><a class="footnote-ref" href="#fn:108">108</a></sup> (PRD)]</strong> - Optimizing the Placement of Numerical Relativity Simulations Using a Mismatch Predicting Neural Network</li> 390<li><strong>[Tissino et al. (2022) <sup id="fnref:109"><a class="footnote-ref" href="#fn:109">109</a></sup> (2210.15684)]</strong> - Combining Effective-One-Body Accuracy and Reduced-Order-Quadrature Speed for Binary Neutron Star Merger Parameter Estimation with Machine Learning</li> 391<li><strong>[Pereira & Sturani (2022) <sup id="fnref:110"><a class="footnote-ref" href="#fn:110">110</a></sup> (2210.07299)]</strong> - Deep Learning Waveform Anomaly Detector for Numerical Relativity Catalogs</li> 392<li><strong>[Katz et al. (2022) <sup id="fnref:111"><a class="footnote-ref" href="#fn:111">111</a></sup> (PRD)]</strong> - Fast Extreme-Mass-Ratio-Inspiral Waveforms: New Tools for Millihertz Gravitational-Wave Data Analysis</li> 393</ul> 394</li> 395</ul> 396<h2 id="signal_detection_bbhs">Signal Detection (BBHs)<a class="headerlink" href="#signal_detection_bbhs" title="Permanent link">¶</a></h2> 397<blockquote></blockquote> 398<ul> 399<li> 400<p>
400Matched filter searches:</p> 401<ul> 402<li>Searches for gravitational wave signals from compact binaries use matched filtering. GW detector noise is non-Stationary and non-Gaussian.</li> 403<li>Discrete template bank is built to cover the mass-spin parameter space for potential sources. Density of the bank is determined by the minimum overlap requirement of 0.97 so only 3% of SNR is lost. Assume spins are aligned to the orbital angular momentum. Do not account for tidal deformability of neutron stars.</li> 404<li>Existing matched filtering searches are close to optimal and quite fast (~1 min latency): Matched filtering is very close to optimal sensitivity; Not all of the parameter space is covered; Non-Gaussian noise is well understood;</li> 405<li>ML searches can help if they are fast and do as well on detector noise artefacts.<blockquote></blockquote> 406</li> 407</ul> 408</li> 409<li> 410<p>Machine learning CBC searches:</p> 411<p>Binary black holes are easy but binary neutron stars are hard - they are longer in duration and broader in bandwidth; Spin (with precession) and eccentricity expand the parameter space; Point estimate parameter estimation is useless - sorry to be harsh; The background estimation problem - a long standing issue that people feel quite strongly about; </p> 412<ul> 413<li><strong>[George & Huerta (2018) <sup id="fnref:112"><a class="footnote-ref" href="#fn:112">112</a></sup> (PRD)]</strong> use a system of two deep convolutional neural networks to rapidly detect CBC signals. They use time series as input so that they can find signals too small for image recognition. They find their method significantly outperforms conventional machine learning techniques, achieves similar performance compared to matched-filtering while being several orders of magnitude faster.<ul> 414<li>Deep learning for real-time classification and regression of gravitational waves in simulated LIGO noise. <strong>[George & Huerta (2018) <sup id="fnref2:112"><a class="footnote-ref" href="#fn:112">112</a></sup> (PRD)]</strong></li> 415<li>Deep learning for real-time classification and regression of gravitational waves in real advanced LIGO noise. <strong>[George & Huerta (2018) <sup id="fnref:113"><a class="footnote-ref" href="#fn:113">113</a></sup> (PLB) <sup id="fnref:114"><a class="footnote-ref" href="#fn:114">114</a></sup> (1711.07966); George & Huerta (2018) [@George2017vlv] (NiPS Summer School)]</strong></li> 416<li>Deep learning at scale for real-time gravitational wave parameter estimation and tests of general relativity <strong>[Shen et al. (2019) <sup id="fnref:115"><a class="footnote-ref" href="#fn:115">115</a></sup> (Mach. learn.: sci. technol.)].</strong> First Bayesian Neural Network model at scale to characterize a 4-D signal manifold with 10M+ templates. Trained with over ten million waveforms using 1024 nodes (64 processors/node) on an HPC platform optimized for deep learning researches (Theta at Argonne National Lab). Inference time is 2 milliseconds for each gravitational wave detection using a single GPU.</li> 417</ul> 418</li> 419<li><strong>[Gabbard et al. (2018) <sup id="fnref:116"><a class="footnote-ref" href="#fn:116">116</a></sup> (PRL)]</strong> also perform a CBC search with a basic and standard CNN network to learn to classify between noise and signal+noise classes. They use whitened time series of measured gravitational-wave strain as an input. Train and test on simulated binary black hole signals in synthetic Gaussian LIGO noise. They find they can reproduce the sensitivity of a matched filter search. i.e. the CNN approach can achieve the same sensitivity as a matched filtering analysis. Classification of 2-D BBH signals in simulated LIGO noise. </li> 420<li><strong>[Li et al. (2020) <sup id="fnref:117"><a class="footnote-ref" href="#fn:117">117</a></sup> (Front. Phys.)]</strong> - Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network</li> 421<li><strong>[Kapadia et al. (2017) <sup id="fnref:118"><a class="footnote-ref" href="#fn:118">118</a></sup> (PRD)]</strong> - Classifier for Gravitational-wave Inspiral Signals in Nonideal Single-detector Data</li> 422<li><strong>[Cao et al. (2018) <sup id="fnref:119"><a class="footnote-ref" href="#fn:119">119</a></sup> (JHNU)]</strong> - Initial study on the application of deep learning to the Gravitational Wave data analysis</li> 423<li><strong>[Fan et al. (2019) <sup id="fnref2:120"><a class="footnote-ref" href="#fn:120">120</a></sup> (SCI CHINA PHYS MECH)]</strong> - Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors</li> 424<li><strong>[Luo et al. (2019) <sup id="fnref:121"><a class="footnote-ref" href="#fn:121">121</a></sup> (Front. Phys.)]</strong> - Extraction of gravitational wave signals with optimized convolutional neural network</li> 425<li><strong>[Lin et al. (2019) <sup id="fnref:122"><a class="footnote-ref" href="#fn:122">
425122</a></sup> (Front. Phys.)]</strong> - Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis and Convolutional Neural Networks</li> 426<li><strong>[Wang et al. (2019) <sup id="fnref2:123"><a class="footnote-ref" href="#fn:123">123</a></sup> (New J. Phys.)]</strong> - Identifying Extra High Frequency Gravitational Waves Generated from Oscillons with Cuspy Potentials Using Deep Neural Networks</li> 427<li><strong>[Rebei et al. (2019) <sup id="fnref:124"><a class="footnote-ref" href="#fn:124">124</a></sup> (PRD)]</strong> - Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers</li> 428<li><strong>[Krastev (2020) <sup id="fnref:125"><a class="footnote-ref" href="#fn:125">125</a></sup> (PLB)]</strong> - Real-time Detection of Gravitational Waves from Binary Neutron Stars Using Artificial Neural Networks</li> 429<li><strong>[Mytidis et al. (2019) <sup id="fnref:126"><a class="footnote-ref" href="#fn:126">126</a></sup> (PRD)]</strong> - Sensitivity Study Using Machine Learning Algorithms on Simulated <span><span class="MathJax_Preview">r</span>
429<script type="math/tex">r</script>
429</span>-mode Gravitational Wave Signals from Newborn Neutron Stars</li> 430<li><strong>[Gebhard et al. (2017) <sup id="fnref:127"><a class="footnote-ref" href="#fn:127">127</a></sup> (Workshop)]</strong> - Convwave: Searching for gravitational waves with fully convolutional neural nets</li> 431<li><strong>[Gebhard et al. (2019) <sup id="fnref:128"><a class="footnote-ref" href="#fn:128">128</a></sup> (PRD)]</strong> - Convolutional Neural Networks: A Magic Bullet for Gravitational-wave Detection?</li> 432<li><strong>[Bresten & Jung (2019) <sup id="fnref:129"><a class="footnote-ref" href="#fn:129">129</a></sup> (1910.08245)]</strong> - Detection of Gravitational Waves Using Topological Data Analysis and Convolutional Neural Network: An Improved Approach</li> 433<li><strong>[Santos et al. 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(2020) <sup id="fnref:134"><a class="footnote-ref" href="#fn:134">134</a></sup> (PRD)]</strong> - Gravitational-Wave Signal Recognition of LIGO Data by Deep Learning</li> 438<li><strong>[Kim et al. (2020) <sup id="fnref:135"><a class="footnote-ref" href="#fn:135">135</a></sup> (PRD)]</strong> - Ranking Candidate Signals with Machine Learning in Low-latency Searches for Gravitational Waves from Compact Binary Mergers</li> 439<li><strong>[Schäfer (2019) <sup id="fnref2:136"><a class="footnote-ref" href="#fn:136">136</a></sup> (Masters Thesis)]</strong> - Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning</li> 440<li><strong>[Schäfer et al. 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(2020) <sup id="fnref:144"><a class="footnote-ref" href="#fn:144">144</a></sup> (PRD)]</strong> - Improving Significance of Binary Black Hole Mergers in Advanced Ligo Data Using Deep Learning : Confirmation of GW151216</li> 449<li><strong>[Wong et al. (2020) <sup id="fnref:145"><a class="footnote-ref" href="#fn:145">145</a></sup> (2007.10350)]</strong> - Gravitational-wave signal-to-noise interpolation via neural networks</li> 450<li><strong>[Wei et al. (2021) <sup id="fnref:146"><a class="footnote-ref" href="#fn:146">146</a></sup> (PLB)]</strong> - Deep Learning for Gravitational Wave Forecasting of Neutron Star Mergers</li> 451<li><strong>[Cabero et al. (2020) <sup id="fnref:147"><a class="footnote-ref" href="#fn:147">147</a></sup> (ApJ)]</strong> - GWSkyNet: A Real-time Classifier for Public Gravitational-wave Candidates</li> 452<li><strong>[Kim et al. (2020) <sup id="fnref:148"><a class="footnote-ref" href="#fn:148">148</a></sup> (ApJ)]</strong> - Identification of Lensed Gravitational Waves with Deep Learning</li> 453<li><strong>[Wei et al. (2021) <sup id="fnref:149"><a class="footnote-ref" href="#fn:149">149</a></sup> (PLB)]</strong> - Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers</li> 454<li><strong>[Xia et al. (2020) <sup id="fnref:150"><a class="footnote-ref" href="#fn:150">150</a></sup> (PRD)]</strong> - Improved Deep Learning Techniques in Gravitational-wave Data Analysis</li> 455<li><strong>[Alvares et al. (2020) <sup id="fnref2:151"><a class="footnote-ref" href="#fn:151">151</a></sup> (CQG)]</strong> - Exploring Gravitational-wave Detection and Parameter Inference Using Deep Learning Methods</li> 456<li><strong>[Wang et al. (2019) <sup id="fnref3:123"><a class="footnote-ref" href="#fn:123">123</a></sup> (New J. Phys.)]</strong> - Identifying Extra High Frequency Gravitational Waves Generated from Oscillons with Cuspy Potentials Using Deep Neural Networks</li> 457<li>LIGO & Virgo provide two probabilities in low-latency. <strong>[Chatterjee et al. (2020) [@chatterjee2020machine] (ApJ)]</strong> The probability that there is a neutron star in the CBC system, P(HasNS). The probability that there exists tidally disrupted matter outside the final coalesced object after the merger, P(HasRemnant). Matched filter searches give point estimates of mass and spin but they have large errors! To solve this a machine learning classification is used. (scikit learn K nearest neighbours, also tried random forest). A training set is created by injecting fake signals into gravitational wave data and performing a search. This then produces a map between true values and matched filter search point estimates which is learnt by the classifier.</li> 458<li><strong>[Wei et al. (2020) <sup id="fnref:152"><a class="footnote-ref" href="#fn:152">152</a></sup> (ApJ)]</strong> - Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence</li> 459<li><strong>[Menéndez-Vázquez et al. (2020) <sup id="fnref:153"><a class="footnote-ref" href="#fn:153">153</a></sup> (PRD)]</strong> - Searches for Compact Binary Coalescence Events Using Neural Networks in the LIGO/Virgo Second Observation Period</li> 460<li><strong>[Krastev et al. (2020) <sup id="fnref:154"><a class="footnote-ref" href="#fn:154">154</a></sup> (PLB)]</strong> - Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data Using Deep Learning</li> 461<li><strong>[Dodia (2021) <sup id="fnref:155"><a class="footnote-ref" href="#fn:155">155</a></sup> (2101.00195)]</strong> - Detecting Residues of Cosmic Events Using Residual Neural Network</li> 462<li><strong>[Kulkarni et al. (2019) <sup id="fnref:156"><a class="footnote-ref" href="#fn:156">156</a></sup> (PRD)]</strong> - Random Projections in Gravitational Wave Searches of Compact Binaries (<strong>Random projections</strong>)</li> 463<li><strong>[Rzeza et al. (2021) <sup id="fnref:157"><a class="footnote-ref" href="#fn:157">157</a></sup> (2101.03226)]</strong>
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(2022) <sup id="fnref:175"><a class="footnote-ref" href="#fn:175">175</a></sup> (IEEE Access)]</strong> - Detection of Non-stationary GW Signals in High Noise from Cohenâs Class of Time-frequency Representations Using Deep Learning</li> 484<li><strong>[Dahal (2022) <sup id="fnref:176"><a class="footnote-ref" href="#fn:176">176</a></sup> (2201.04086)]</strong> - Application of Common Spatial Patterns in Gravitational Waves Detection</li> 485<li><strong>[Chaurvedi et al. (2022) <sup id="fnref:177"><a class="footnote-ref" href="#fn:177">177</a></sup> (Front. Artif. Intell.)]</strong> - Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale</li> 486<li><strong>[Zhang et al. (2022) <sup id="fnref:178"><a class="footnote-ref" href="#fn:178">178</a></sup> (PRD)]</strong> - Detecting Gravitational-waves from Extreme Mass Ratio Inspirals Using Convolutional Neural Networks</li> 487<li><strong>[Safarzadeh et al. (2022) <sup id="fnref:179"><a class="footnote-ref" href="#fn:179">179</a></sup> (2202.07399)]</strong> - Interpreting a Machine Learning Model for Detecting Gravitational Waves</li> 488<li><strong>[Choudhary et al. (2022) <sup id="fnref:180"><a class="footnote-ref" href="#fn:180">180</a></sup> (PRD)]</strong> - Deep Learning Network to Distinguish Binary Black Hole Signals from Short-Duration Noise Transients</li> 489<li><strong>[McIsaac & Harry (2022) <sup id="fnref:181"><a class="footnote-ref" href="#fn:181">181</a></sup> (2203.03449)]</strong> - Using Machine Learning to Auto-Tune Chi-Squared Tests for Gravitational Wave Searches</li> 490<li><strong>[Jiang et al. (2022) <sup id="fnref:182"><a class="footnote-ref" href="#fn:182">182</a></sup> (Front. Phys.)]</strong> - Identify Real Gravitational Wave Events in the LIGO-Virgo Catalog GWTC-1 and GWTC-2 with Convolutional Neural Network</li> 491<li><strong>[Ma et al. (2022) [@2022MaEnsembleDeepConvolutional] (PRD)]</strong> - Ensemble of Deep Convolutional Neural Networks for Real-Time Gravitational Wave Signal Recognition</li> 492<li><strong>[Baltus et al. (2022) <sup id="fnref:183"><a class="footnote-ref" href="#fn:183">183</a></sup> (PRD)]</strong> - Convolutional Neural Network for Gravitational-Wave Early Alert: Going down in Frequency</li> 493<li><strong>[Kim et al. (2022) <sup id="fnref:184"><a class="footnote-ref" href="#fn:184">184</a></sup> (ApJ)]</strong> - Deep Learning-Based Search for Microlensing Signature from Binary Black Hole Events in GWTC-1 and -2</li> 494<li><strong>[Verma et al. (2022) <sup id="fnref:185"><a class="footnote-ref" href="#fn:185">185</a></sup> (2206.12673)]</strong> - Can Convolution Neural Networks Be Used for Detection of Gravitational Waves from Precessing Black Hole Systems?</li> 495<li><strong>[Aveiro et al. (2022) <sup id="fnref:186"><a class="footnote-ref" href="#fn:186">186</a></sup> (PRD)]</strong> - Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using Object-Detection Machine Learning Models</li> 496<li><strong>[Andrews et al. (2022) <sup id="fnref:187"><a class="footnote-ref" href="#fn:187">187</a></sup> (2207.04749)]</strong> - DeepSNR: A Deep Learning Foundation for Offline Gravitational Wave Detection</li> 497<li><strong>[Zhao et al. (2022) <sup id="fnref:188"><a class="footnote-ref" href="#fn:188">188</a></sup> (Communications Physics)]</strong> - Space-Based Gravitational Wave Signal Detection and Extraction with Deep Neural Network</li> 498<li><strong>[Jingkai Yan et al. (2022) <sup id="fnref:189"><a class="footnote-ref" href="#fn:189">189</a></sup> (2207.11583)]</strong> - Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks</li> 499<li><strong>[Sharma et al. (2022) <sup id="fnref:190"><a class="footnote-ref" href="#fn:190">190</a></sup> (2208.02545)]</strong> - Fishing Massive Black Hole Binaries with THAMES</li> 500<li><strong>[Santos et al. (2022) <sup id="fnref:191"><a class="footnote-ref" href="#fn:191">191</a></sup> (Expert Syst. Appl.)]</strong> - Gravitational Wave Signal Recognition and Ring-down Time Estimation via Artificial Neural Networks</li> 501<li><strong>[Barone et al. (2022) <sup id="fnref:192"><a class="footnote-ref" href="#fn:192">192</a></sup> (2206.06004)]</strong> - A Novel Multi-Layer Modular Approach for Real-Time Gravitational-Wave Detection</li> 502<li><strong>[Schäfer et al. (2022) <sup id="fnref:193"><a class="footnote-ref" href="#fn:193">193</a></sup> (PRD)]</strong> - First Machine Learning Gravitational-Wave Search Mock Data Challenge</li> 503<li><strong>[Baltus (2022) <sup id="fnref:194"><a class="footnote-ref" href="#fn:194">194</a></sup> (PhD Thesis)]</strong> - A Machine Learning Approach to the Search for Gravitational Waves Emitted by Light Systems</li> 504<li><strong>[Zhang et al. (2022) <sup id="fnref:195"><a class="footnote-ref" href="#fn:195">195</a></sup> (Comput. Intell. Neurosci.)]</strong> - Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network</li> 505<li><strong>[Badger et al. (2022) <sup id="fnref:196"><a class="footnote-ref" href="#fn:196">196</a></sup> (PRL)]</strong> - Dictionary Learning: A Novel Approach to Detecting Binary Black Holes in the Presence of Galactic Noise with LISA</li> 506<li><strong>[Verma et al. (2022) [@2022VermaEmployingdeeplearning] (AIP Conference Proceedings)]</strong> - Employing Deep Learning for Detection of Gravitational Waves from C
506ompact Binary Coalescences</li> 507<li><strong>[Qiu et al. (2022) <sup id="fnref:197"><a class="footnote-ref" href="#fn:197">197</a></sup> (2210.15888)]</strong> - Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers</li> 508<li><strong>[Nousi et al. (2023) <sup id="fnref:198"><a class="footnote-ref" href="#fn:198">198</a></sup> (PRD)]</strong> - Deep Residual Networks for Gravitational Wave Detection</li> 509<li><strong>[Kim (2022) <sup id="fnref:199"><a class="footnote-ref" href="#fn:199">199</a></sup> (2211.02655)]</strong> Search for Microlensing Signature in Gravitational Waves from Binary Black Hole Events</li> 510<li><strong>[Yan et al. (2022) <sup id="fnref:200"><a class="footnote-ref" href="#fn:200">200</a></sup> (Res. Astron. Astrophys)]</strong> - Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR</li> 511<li><strong>[Alhassan et al. (2022) <sup id="fnref:201"><a class="footnote-ref" href="#fn:201">201</a></sup> (2211.13789)]</strong> - Detection of Einstein Telescope Gravitational Wave Signals from Binary Black Holes Using Deep Learning</li> 512<li><strong>[Jiang & Luo (2022) <sup id="fnref:202"><a class="footnote-ref" href="#fn:202">202</a></sup> (ICPR)]</strong> - Convolutional Transformer for Fast and Accurate Gravitational Wave Detection</li> 513<li><strong>[Andres-Carcasona et al. (2022) [@2022Andres-CarcasonaSearchesMassAsymmetricCompact] (PRD)]</strong> - Searches for Mass-Asymmetric Compact Binary Coalescence Events Using Neural Networks in the LIGO/Virgo Third Observation Period</li> 514<li><strong>[Zhang et al. (2022) [@PhysRevD.106.122002] (PRD)]</strong> - Deep Learning Model Based on a Bidirectional Gated Recurrent Unit for the Detection of Gravitational Wave Signals</li> 515<li><strong>[Wang et al. (2023) <sup id="fnref:203"><a class="footnote-ref" href="#fn:203">203</a></sup> (2302.00295)]</strong> - Self-Supervised Learning for Gravitational Wave Signal Identification</li> 516<li><strong>[Ravichandran et al. (2023) <sup id="fnref:204"><a class="footnote-ref" href="#fn:204">204</a></sup> (2302.00666)]</strong> - Rapid Identification and Classification of Eccentric Gravitational Wave Inspirals with Machine Learning</li> 517<li><strong>[Shaikh et al. (2022) <sup id="fnref:205"><a class="footnote-ref" href="#fn:205">205</a></sup> (IEEE)]</strong> - Optimizing Large Gravitational-Wave Classifier through a Custom Cross-System Mirrored Strategy Approach</li> 518<li><strong>[Ma et al. (2023) [@PhysRevD.107.063029] (PRD)]</strong> - Artificial Intelligence Model for Gravitational Wave Search Based on the Waveform Envelope</li> 519<li><strong>[Jin et al. (2023) <sup id="fnref:206"><a class="footnote-ref" href="#fn:206">206</a></sup> (2305.19003)]</strong> - Rapid Identification of Time-Frequency Domain Gravitational Wave Signals from Binary Black Holes Using Deep Learning</li> 520<li><strong>[Jadhav et al. (2023) <sup id="fnref:207"><a class="footnote-ref" href="#fn:207">207</a></sup> (2306.11797)]</strong> - Towards a Robust and Reliable Deep Learning Approach for Detection of Compact Binary Mergers in Gravitational Wave Data</li> 521<li><strong>[Tian et al. (2023) <sup id="fnref:208"><a class="footnote-ref" href="#fn:208">208</a></sup> (2306.15728)]</strong> - Physics-Inspired Spatiotemporal-Graph AI Ensemble for Gravitational Wave Detection</li> 522<li><strong>[Pal & Nayak (2023) <sup id="fnref:209"><a class="footnote-ref" href="#fn:209">209</a></sup> (2307.03736)]</strong> - Swarm-Intelligent Search for Gravitational Waves from Eccentric Binary Mergers</li> 523<li><strong>[Trovato et al. (2023) <sup id="fnref:210"><a class="footnote-ref" href="#fn:210">210</a></sup> (2307.09268)]</strong> - Neural Network Time-Series Classifiers for Gravitational-Wave Searches in Single-Detector Periods</li> 524<li><strong>[Freitas et al. (2023) <sup id="fnref:211"><a class="footnote-ref" href="#fn:211">211</a></sup> (PoS)]</strong> - Comparison of Training Methods for Convolutional Neural Network Model for Gravitational-Wave Detection from Neutron Star-Black Hole Binaries</li> 525</ul> 526</li> 527</ul> 528<h2 id="parameter_estimation_pe">Parameter Estimation (PE)<a class="headerlink" href="#parameter_estimation_pe" title="Permanent link">¶</a></h2> 529<p>Characterized by 15 parameters. Masses, spins, distance, inclination, sky position, polarization. LIGO & Virgo GWTC-1 <strong>[Abbott et al. (2019) <sup id="fnref:212"><a class="footnote-ref" href="#fn:212">212</a></sup> (PRX)]</strong> . Bayes’ Theorem (<a href="https://git.ligo.org/virginia.demilio/pe-tutorial-laac19">LAAC tutorial 2019 - Virginia d’Emilio</a>)</p> 530<blockquote></blockquote> 531<ul> 532<li> 533<p>MCMC and Nested Sampling</p> 534<ul> 535<li>We have two main PE codes LALInference and Bilby. MCMC Random steps are taken in parameter space, according to a proposal distribution, and accepted or rejected according to the Metropolis-Hastings algorithm. Nested sampling can also compute evidence
535s for model selection. <strong>[Skilling (2006) <sup id="fnref:213"><a class="footnote-ref" href="#fn:213">213</a></sup> (Bayesian Anal.)]</strong><blockquote></blockquote> 536</li> 537</ul> 538</li> 539<li> 540<p>Machine Learning Parameter Estimation</p> 541<ul> 542<li><strong>The current âholy grailâ of machine learning for GWs.</strong></li> 543<li>BAMBI: blind accelerated multimodal Bayesian inference combines the benefits of nested sampling and artificial neural networks. <strong>[Graff et al. (2012) <sup id="fnref:214"><a class="footnote-ref" href="#fn:214">214</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> An artificial neural network learns the likelihood function to increase significantly the speed of the analysis. <strong>[Graff (2012) <sup id="fnref:215"><a class="footnote-ref" href="#fn:215">215</a></sup> (PhD Thesis)]</strong></li> 544<li>Chua et al. <strong>[Chua & Vallisneri (2020) <sup id="fnref:216"><a class="footnote-ref" href="#fn:216">216</a></sup> (PRL)]</strong> produce Bayesian posteriors using neural networks.</li> 545<li>Gabbard et al. <strong>[Gabbard et al. (2019) [@2021GabbardBayesianParameterEstimation] (Nature Physics)]</strong> use a conditional variational autoencoder pre-trained on binary black hole signals. We use a variation inference approach to produce samples from the posterior. It does NOT need to be trained on precomputed posteriors. It is ~6 orders of magnitude faster than existing sampling techniques. For Chris Messenger, it seems completely obvious that all data analysis will be ML in 5-10 years.</li> 546<li><strong>[Chatterjee et al. (2020) [@chatterjee2020machine] (ApJ)]</strong> - A Machine Learning-based Source Property Inference for Compact Binary Mergers</li> 547<li><strong>[Fan et al. (2019) <sup id="fnref:120"><a class="footnote-ref" href="#fn:120">120</a></sup> (SCI CHINA PHYS MECH)]</strong> - Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors</li> 548<li><strong>[Green et al. (2020) [@2020GreenGravitationalwaveParameter] (PRD)]</strong> - Gravitational-Wave Parameter Estimation with Autoregressive Neural Network Flows</li> 549<li><strong>[Carrillo et al. (2016) <sup id="fnref:217"><a class="footnote-ref" href="#fn:217">217</a></sup> (GRG)]</strong> - Parameter estimates in binary black hole collisions using neural networks</li> 550<li><strong>[Carrillo et al. (2018) <sup id="fnref:218"><a class="footnote-ref" href="#fn:218">218</a></sup> (INT J MOD PHYS D)]</strong> - One parameter binary black hole inverse problem using a sparse training set</li> 551<li><strong>[Chatterjee et al. (2019) <sup id="fnref:219"><a class="footnote-ref" href="#fn:219">219</a></sup> (PRD)]</strong> - Using deep learning to localize gravitational wave sources</li> 552<li><strong>[Yamamoto & Tanaka (2020) <sup id="fnref:220"><a class="footnote-ref" href="#fn:220">220</a></sup> (2002.12095)]</strong> - Use of conditional variational auto encoder to analyze ringdown gravitational waves</li> 553<li><strong>[Haegel & Husa (2020) <sup id="fnref:221"><a class="footnote-ref" href="#fn:221">221</a></sup> (CQG)]</strong> - Predicting the properties of black-hole merger remnants with deep neural networks</li> 554<li><strong>[Belgacem et al. (2020) <sup id="fnref:222"><a class="footnote-ref" href="#fn:222">222</a></sup> (PRD)]</strong> - Gaussian processes reconstruction of modified gravitational wave propagation</li> 555<li><strong>[Chen et al. (2020) <sup id="fnref:132"><a class="footnote-ref" href="#fn:132">132</a></sup> (Sci. China Phys. Mech. Astron.)]</strong> - Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array</li> 556<li><strong>[Khan et al. (2020) <sup id="fnref:223"><a class="footnote-ref" href="#fn:223">223</a></sup> (PLB)]</strong> - Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers</li> 557<li><strong>[Nakano et al. (2019) <sup id="fnref:224"><a class="footnote-ref" href="#fn:224">224</a></sup> (PRD)]</strong> - Comparison of Various Methods to Extract Ringdown Frequency from Gravitational Wave Data</li> 558<li><strong>[Engels et al. (2014) <sup id="fnref:225"><a class="footnote-ref" href="#fn:225">225</a></sup> (PRD)]</strong> - Multivariate regression analysis of gravitational waves from rotating core collapse</li> 559<li><strong>[Green & Gair (2021) [@2021GreenCompleteParameterInference] (Mach. learn.: sci. technol.)]</strong> - Complete Parameter Inference for GW150914 Using Deep Learning</li> 560<li><strong>[Vivanco et al. (2020) <sup id="fnref:226"><a class="footnote-ref" href="#fn:226">226</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - A Scalable Random Forest Regressor for Combining Neutron-Star Equation of State Measurements: A Case Study with GW170817 and GW190425</li> 561<li><strong>[Delaunoy (2020) <sup id="fnref:227"><a class="footnote-ref" href="#fn:227">227</a></sup> (Master Thesis)]</strong> - Lightning Gravitational Wave Parameter Inference through Neural Amortization</li> 562<li><strong>[Pacilio (2019) <sup id="fnref:228"><a class="footnote-ref" href="#fn:228">228</a></sup> (Master Thesis)]</strong> - Multioutput Regression of Noisy Time Series Using Convolutional Neural Networks with Applications to Gravitational Waves</li> 563<li><strong>[Delaunoy et al. (2020) <sup id="fnref:229"><a class="footnote-ref" href="#fn:229">229</a></sup> (2010.12931)]</strong> - Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization</li> 564<li><strong>[Marulanda et al. (2020) <sup id="fnref:133"><a class="footnote-ref" href="#fn:133">133</a></sup> (PLB)]</strong> - Deep Learning Merger Masses Estimation from Gravitational Waves Signals in the Frequency Domain</li> 565<li><strong>[Jeffrey & Wandelt (2020) <sup id="fnref:230"><a class="footnote-ref" href="#fn:230">230</a></sup> (NeurIPS)]</strong> - Solving High-dimensional Parameter Inference: Marginal Posterior Densities & Moment Networks</li> 566<li><strong>[Alvares et al. (2020) <sup id="fnref:151"><a class="footnote-ref" href="#fn:151">151</a></sup> (CQG)]</strong> - Exploring Gravitational-wave Detection and Parameter Inference Using Deep Learning Methods</li> 567<li><strong>[Wang et al. (2019) <sup id="fnref:123"><a class="footnote-ref" href="#fn:123">123</a></sup> (New J. Phys.)]</strong> - Identifying Extra High Frequency Gravitational Waves Generated from Oscillons with Cuspy Potentials Using Deep Neural Networks</li> 568<li><strong>[Bhagwat & Pacilio (2021) [@2021BhagwatMergerringdownConsistency] (PRD)]</strong> - Merger-Ringdown Consistency: A New Test of Strong Gravity Using Deep Learning</li> 569<li><strong>[Williams et al. (2021) <sup id="fnref:231"><a class="footnote-ref" href="#fn:231">231</a></sup> (PRD)]</strong> - Nested Sampling with Normalising Flows for Gravitational-wave Inference</li> 570<li><strong>[D’Emilio et al. (2021) <sup id="fnref:232"><a class="footnote-ref" href="#fn:232">
570232</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - Density Estimation with Gaussian Processes for Gravitational-wave Posteriors</li> 571<li><strong>[Dax et al. (2021) [@2021DaxRealtimeGravitational] (PRL)]</strong> - Real-time Gravitational-wave Science with Neural Posterior Estimation</li> 572<li><strong>[Kuo & Lin (2021) <sup id="fnref:233"><a class="footnote-ref" href="#fn:233">233</a></sup> (2107.10730)]</strong> - Conditional Noise Deep Learning for Parameter Estimation of Gravitational Wave Events</li> 573<li><strong>[Gunny et al. (2021) <sup id="fnref:234"><a class="footnote-ref" href="#fn:234">234</a></sup> (2108.12430)]</strong> - Hardware-accelerated Inference for Real-time Gravitational-wave Astronomy</li> 574<li><strong>[Rozet & Louppe (2021) <sup id="fnref:235"><a class="footnote-ref" href="#fn:235">235</a></sup> (2110.00449)]</strong> - Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inferenceå°±æ¯</li> 575<li><strong>[Guedes et al. (2021) <sup id="fnref:236"><a class="footnote-ref" href="#fn:236">236</a></sup> (SBIC)]</strong> - Mass Determination of Cosmological Objects from Gravitational Wave Data Using Neural Networks</li> 576<li><strong>[Kolmus et al. (2021) [@2021KolmusSwiftskylocalization] (2111.00833)]</strong> - Swift Sky Localization of Gravitational Waves Using Deep Learning Seeded Importance Sampling</li> 577<li><strong>[Dax et al. (2021) [@2021DaxGroupEquivariantNeural] (2111.13139)]</strong> - Group Equivariant Neural Posterior Estimation</li> 578<li><strong>[Khan et al. (2021) <sup id="fnref:237"><a class="footnote-ref" href="#fn:237">237</a></sup> (PLB)]</strong> - AI and Extreme Scale Computing to Learn and Infer the Physics of Higher Order Gravitational Wave Modes of Quasi-Circular, Spinning, Non-Precessing Black Hole Mergers</li> 579<li><strong>[Wang et al. (2022) <sup id="fnref:238"><a class="footnote-ref" href="#fn:238">238</a></sup> (BDMA)]</strong> - Sampling with Prior Knowledge for High-dimensional Gravitational Wave Data Analysis</li> 580<li><strong>[McLeod et al. (2022) <sup id="fnref:239"><a class="footnote-ref" href="#fn:239">239</a></sup> (2201.11126)]</strong> - Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning</li> 581<li><strong>[Sasaoka et al. (2022) <sup id="fnref:240"><a class="footnote-ref" href="#fn:240">240</a></sup> (PRD)]</strong> - Localization of Gravitational Waves Using Machine Learning</li> 582<li><strong>[Guo et al. (2022) <sup id="fnref:241"><a class="footnote-ref" href="#fn:241">241</a></sup> (2203.06969)]</strong> - Mimicking Mergers: Mistaking Black Hole Captures as Mergers</li> 583<li><strong>[Karamanis et al. (2022) <sup id="fnref:242"><a class="footnote-ref" href="#fn:242">242</a></sup> (2207.05652)]</strong> - Accelerating Astronomical and Cosmological Inference with Preconditioned Monte Carlo</li> 584<li><strong>[Chatterjee et al. (2022) <sup id="fnref:243"><a class="footnote-ref" href="#fn:243">243</a></sup> (2207.14522)]</strong> - Rapid Localization of Gravitational Wave Sources from Compact Binary Coalescences Using Deep Learning</li> 585<li><strong>[Tsatsev (2022) <sup id="fnref:244"><a class="footnote-ref" href="#fn:244">244</a></sup> (Masters Thesis)]</strong> - Parameter Inference of Gravitational Waves Using Inverse Autoregressive Spline Flow</li> 586<li><strong>[Bayley et al. (2022) <sup id="fnref:245"><a class="footnote-ref" href="#fn:245">245</a></sup> (PRD)]</strong> - Rapid Parameter Estimation for an All-Sky Continuous Gravitational Wave Search Using Conditional Varitational Auto-Encoders</li> 587<li><strong>[Dax et al. (2022) [@2022DaxNeuralImportanceSampling] (PRL)]</strong> - Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference</li> 588<li><strong>[Wofford et al. (2022) <sup id="fnref:246"><a class="footnote-ref" href="#fn:246">246</a></sup> (PRD)]</strong> - Improving Performance for Gravitational-Wave Parameter Inference with an Efficient and Highly-Parallelized Algorithm</li> 589<li><strong>[Wildberger et al. (2022) [@2022WildbergerAdaptingnoisedistribution] (PRD)]</strong> - Adapting to Noise Distribution Shifts in Flow-Based Gravitational-Wave Inference</li> 590<li><strong>[Langenorff et al. (2022) <sup id="fnref:247"><a class="footnote-ref" href="#fn:247">247</a></sup> (PRL)]</strong> - Normalizing Flows as an Avenue to Studying Overlapping Gravitational Wave Signals</li> 591<li><strong>[Chatterjee et al. (2023) <sup id="fnref:248"><a class="footnote-ref" href="#fn:248">248</a></sup> (2301.03558)]</strong> - Pre-Merger Sky Localization of Gravitational Waves from Binary Neutron Star Mergers Using Deep Learning</li> 592<li><strong>[Wong et al. (2023) <sup id="fnref:249"><a class="footnote-ref" href="#fn:249">249</a></sup> (2302.05333)]</strong> - Fast Gravitational Wave Parameter Estimation without Compromises</li> 593<li><strong>[Williams et al. (2023) <sup id="fnref:250"><a class="footnote-ref" href="#fn:250">250</a></sup> (Mach. learn.: sci. technol.)]</strong> - Importance Nested Sampling with Normalising Flows</li> 594<li><strong>[Bhardwaj et al. (2023) [@2023BhardwajPeregrineSequentialsimulationbased] (PRD)]</strong> - Sequential simulation-based inference for gravitational wave signals</li> 595<li><strong>[Crisostomi et al. (2023) [@2023CrisostomiNeuralPosteriorEstimationa] (PRD)]</strong> - Neural Posterior Estimation with Guaranteed Exact Coverage: The Ringdown of GW150914</li> 596<li><strong>[Dax et al. (2023) [@2023DaxFlowMatchingScalable] (2305.17161)]</strong> - Flow Matching for Scalable Simulation-Based Inference</li> 597<li><strong>[Liu et al. (2023) <sup id="fnref:251"><a class="footnote-ref" href="#fn:251">251</a></sup> (2307.07233)]</strong> - Improving the Scalability of Gaussian-process Error Marginalization in Gravitational-Wave Inference</li> 598<li><strong>[Soma et al. (2023) <sup id="fnref:252"><a class="footnote-ref" href="#fn:252">252</a></sup> (2306.17488)]</strong> - Mass and Tidal Parameter Extraction from Gravitational Waves of Binary Neutron Stars Mergers Using Deep Learning</li> 599<li><strong>[Sun et al. (2023) <sup id="fnref:253"><a class="footnote-ref" href="#fn:253">253</a></sup> (2307.16437)]</strong> - Deep Learning Forecasts of Cosmic Acceleration Parameters from DECi-hertz Interferometer Gravitational-wave Observatory</li> 600<li><strong>[Du et al. (2023) <sup id="fnref:254"><a class="footnote-ref" href="#fn:254">254</a></sup> (2308.05510)]</strong> - Advancing Space-Based Gravitational Wave Astronomy: Rapid Detection and Parameter Estimation Using Normalizing Flows</li> 601<li><strong>[Whittaker et al. (2022) [@PhysRevD.105.124021] (PRD)]</strong> - Using Machine Learning to Parametrize Postmerger Signals from Binary Neutron Stars</li> 602</ul> 603</li> 604</ul> 605<h2 id="population_studies">Population Studies<a class="headerlink" href="#population_studies" title="Permanent link">¶</a></h2> 606<ul> 607<li><strong>[Vinciguerra et al. (2017) <sup id="fnref:255"><a class="footnote-ref" href="#fn:255">255</a></sup> (CQG)]</strong> - Enhancing the significance of gravitational wave bursts through signal classification</li> 608<li>Now that we have started to detect a population of black hole signals we can try to do population studies to try and understand signals formation mechanisms. Population properties paper from O1+02 <strong>[Abbott et al. (2019) <sup id="fnref:256"><a class="footnote-ref" href="#fn:256">256</a></sup> (ApJ)]</strong> . Uses phenomenological models (like power laws) combined with Bayesian hierarchical modelling. Bayesian hierarchical modelling involves some assumptions of populations mass and spin distributions. Does not scale well for high dimensional models and a large number of GW detections. </li> 609<li>We can use unmodelling clustering! In <strong>[Powell et al. (2019) <sup id="fnref:257"><a class="footnote-ref" href="#fn:257">257</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> we apply unmodelled clustering to masses and spins. Two of the populations have identical mass distributions and different spin. This is difficult because spin is poorly measured. Determine the number of populations and the number of CBC signals in each population.</li> 610<li>(How do I try it myself?) The Gravitational Wave Open Science Center has the data, parameter estimates, and matched filtering tutorials that you can download. You can get <a href="https://git.ligo.org/daniel.wysocki/synthetic-PE-posteriors">
610code</a> to produce synthetic parameter estimates for compact binaries.</li> 611<li><strong>[Varma et al. (2019) <sup id="fnref:258"><a class="footnote-ref" href="#fn:258">258</a></sup> (PRL)]</strong> - High-Accuracy Mass, Spin, and Recoil Predictions of Generic Black-Hole Merger Remnants</li> 612<li><strong>[Deligiannidis et al. (2019) <sup id="fnref:259"><a class="footnote-ref" href="#fn:259">259</a></sup> (ICAI)]</strong> - Case Study: Skymap Data Analysis</li> 613<li><strong>[Wong & Gerosa (2019) <sup id="fnref:260"><a class="footnote-ref" href="#fn:260">260</a></sup> (PRD)]</strong> - Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: A case study</li> 614<li><strong>[Wong et al. (2020) <sup id="fnref:261"><a class="footnote-ref" href="#fn:261">261</a></sup> (PRD)]</strong> - Gravitational-wave population inference with deep flow-based generative network</li> 615<li><strong>[Fasano et al. (2020) <sup id="fnref:262"><a class="footnote-ref" href="#fn:262">262</a></sup> (PRD)]</strong> - Distinguishing Double Neutron Star from Neutron Star-black Hole Binary Populations with Gravitational Wave Observations</li> 616<li><strong>[Tiwari (2020) <sup id="fnref:263"><a class="footnote-ref" href="#fn:263">263</a></sup> (CQG)]</strong> - VAMANA: Modeling Binary Black Hole Population with Minimal Assumptions</li> 617<li><strong>[Vernardos et al. (2020) <sup id="fnref:264"><a class="footnote-ref" href="#fn:264">264</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - Quantifying the Structure of Strong Gravitational Lens Potentials with Uncertainty-aware Deep Neural Networks</li> 618<li><strong>[Wong et al. (2020) <sup id="fnref:265"><a class="footnote-ref" href="#fn:265">265</a></sup> (PRD)]</strong> - Constraining the Primordial Black Hole Scenario with Bayesian Inference and Machine Learning: The GWTC-2 Gravitational Wave Catalog</li> 619<li><strong>[Arjona et al. (2021) <sup id="fnref:266"><a class="footnote-ref" href="#fn:266">266</a></sup> (PRD)]</strong> - Machine Learning Forecasts of the Cosmic Distance Duality Relation with Strongly Lensed Gravitational Wave Events</li> 620<li><strong>[Gerosa et al. (2020) <sup id="fnref:267"><a class="footnote-ref" href="#fn:267">267</a></sup> (PRD)]</strong> - Gravitational-wave Selection Effects Using Neural-network Classifiers</li> 621<li><strong>[Talbot & Thrane (2020) <sup id="fnref:268"><a class="footnote-ref" href="#fn:268">268</a></sup> (ApJ)]</strong> - Flexible and Accurate Evaluation of Gravitational-wave Malmquist Bias with Machine Learning</li> 622<li><strong>[Ãlvares et al. (2021) <sup id="fnref:269"><a class="footnote-ref" href="#fn:269">269</a></sup> (IEEE)]</strong> - Gravitational-Wave Parameter Inference Using Deep Learning</li> 623<li><strong>[Cheung et al. (2021) <sup id="fnref:270"><a class="footnote-ref" href="#fn:270">270</a></sup> (2112.06707)]</strong> - Testing the Robustness of Simulation-based Gravitational-wave Population Inference</li> 624<li><strong>[Wong et al .(2020) <sup id="fnref:271"><a class="footnote-ref" href="#fn:271">271</a></sup> (PRD)]</strong> - Joint Constraints on the Field-cluster Mixing Fraction, Common Envelope Efficiency, and Globular Cluster Radii from a Population of Binary Hole Mergers Via Deep Learning</li> 625<li><strong>[Mould et al. (2022) <sup id="fnref:272"><a class="footnote-ref" href="#fn:272">272</a></sup> (2203.03651)]</strong> - Deep Learning and Bayesian Inference of Gravitational-Wave Populations: Hierarchical Black-Hole Mergers</li> 626<li><strong>[Strub et al. (2022) <sup id="fnref:273"><a class="footnote-ref" href="#fn:273">273</a></sup> (2204.04467)]</strong> - Bayesian Parameter-Estimation of Galactic Binaries in LISA Data with Gaussian Process Regression</li> 627<li><strong>[Nakama (2022) <sup id="fnref:274"><a class="footnote-ref" href="#fn:274">274</a></sup> (PRD)]</strong> - Machine Learning Primordial Black Hole Formation</li> 628<li><strong>[Wong et al. (2022) <sup id="fnref:275"><a class="footnote-ref" href="#fn:275">275</a></sup> (2207.12409)]</strong> - Automated Discovery of Interpretable Gravitational-Wave Population Models</li> 629<li><strong>[Mohite (2022) <sup id="fnref:276"><a class="footnote-ref" href="#fn:276">276</a></sup> (PhD Thesis)]</strong> - Data-Driven Population Inference from Gravitational-Wave Sources and Electromagnetic Counterparts</li> 630<li><strong>[Ruhe et al. (2022) <sup id="fnref:277"><a class="footnote-ref" href="#fn:277">277</a></sup> (2211.09008)]</strong> - Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study</li> 631<li><strong>[Edelman et al. (2022) <sup id="fnref:278"><a class="footnote-ref" href="#fn:278">278</a></sup> (2210.12834)]</strong> - Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population</li> 632<li><strong>[Ray et al. (2023) <sup id="fnref:279"><a class="footnote-ref" href="#fn:279">279</a></sup> (2304.08046)]</strong> - Non-Parametric Inference of the Population of Compact Binaries from Gravitational Wave Observations Using Binned Gaussian Processes</li> 633</ul> 634<hr /> 635<h1 id="5_continuous_wave_search">5. Continuous Wave Search<a class="headerlink" href="#5_continuous_wave_search" title="Permanent link">¶</a></h1> 636<ul> 637<li>Existing challenges and signal characteristics: Vast parameter space, The parameter space is incredibly large; Likely very weak signals, The signal is incredibly weak - orders of magnitude lower than the noise amplitude; Leading to traditional searches optimised at fixed computational cost - Generally slow, The dataset is (quite) large, 1year X 1kHz =~10 GB; In the era of open data the LVC and competitors are keen to analyse the data very quickly; Narrow band, However, since we have been limited until now by computational expense - with ML this could no longer be a limit, and hence sensitivity can really improve</li> 638<li><strong>[Morawski et al. (2020) <sup id="fnref:280"><a class="footnote-ref" href="#fn:280">280</a></sup> (Mach. learn.: sci. technol.)]</strong> - Convolutional Neural Network Classifier for the Output of the Time-domain F-statistic All-sky Search for Continuous Gravitational Waves</li> 639<li><strong>[Dreissigacker et al. (2019) <sup id="fnref:281"><a class="footnote-ref" href="#fn:281">281</a></sup> (PRD)]</strong>
639 (Modelled searches) Based on the success of CNNs for compact binary searches; The task is significantly more difficult here; Fair comparison with fully coherent searches over a broad parameter space; The ML approach is reasonably competitive for the simplest of the cases studied; For 10^6 sec observations at 1kHz perform significantly worse than matched filtering</li> 640<li><strong>[Dreissigacker & Prix (2020) <sup id="fnref:282"><a class="footnote-ref" href="#fn:282">282</a></sup> (PRD)]</strong> - Deep-learning continuous gravitational waves: Multiple detectors and realistic noise</li> 641<li><strong>[Bayley et al. (2019) <sup id="fnref:283"><a class="footnote-ref" href="#fn:283">283</a></sup> (PRD)]</strong> (Unmodelled searches) A very weakly modelled search for weak psuedo-sinusoidal continuous signals; Uses the Viterbi algorithm to efficiently find the maximum sum of power/ statistic across a time-frequency plane (hence SOAP); Requires no templates and runs on ârawâ GW data; Is exceptionally good at finding detector line features (also annoying); Extension work applies a CNN to the output for better signal vs line discrimination;</li> 642<li><strong>[Miller et al. (2019) <sup id="fnref:284"><a class="footnote-ref" href="#fn:284">284</a></sup> (PRD)]</strong> - How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?</li> 643<li><strong>[Miller (2019) <sup id="fnref:285"><a class="footnote-ref" href="#fn:285">285</a></sup> (PhD Thesis)]</strong> - Using Machine Learning and the Hough Transform to Search for Gravitational Waves Due to R-mode Emission by Isolated Neutron Stars</li> 644<li><strong>[Schafer (2019) <sup id="fnref:136"><a class="footnote-ref" href="#fn:136">136</a></sup> (Masters Thesis)]</strong> - Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning</li> 645<li><strong>[Beheshtipour & Papa (2020) <sup id="fnref:286"><a class="footnote-ref" href="#fn:286">286</a></sup> (PRD)]</strong> - Deep Learning for Clustering of Continuous Gravitational Wave Candidates</li> 646<li><strong>[Middleton et al. (2020) <sup id="fnref:287"><a class="footnote-ref" href="#fn:287">287</a></sup> (PRD)]</strong> - Search for Gravitational Waves from Five Low Mass X-ray Binaries in the Second Advanced Ligo Observing Run with an Improved Hidden Markov Model</li> 647<li><strong>[Bayley et al. (2020) <sup id="fnref:288"><a class="footnote-ref" href="#fn:288">288</a></sup> (PRD)]</strong> - Robust Machine Learning Algorithm to Search for Continuous Gravitational Waves</li> 648<li><strong>[Bayley (2020) <sup id="fnref:289"><a class="footnote-ref" href="#fn:289">289</a></sup> (PhD Thesis)]</strong> - Non-parametric and Machine Learning Techniques for Continuous Gravitational Wave Searches</li> 649<li><strong>[Jones & Sun (2020) <sup id="fnref:290"><a class="footnote-ref" href="#fn:290">290</a></sup> (2007.08732)]</strong> - Search for Continuous Gravitational Waves from Fomalhaut B in the Second Advanced Ligo Observing Run with a Hidden Markov Model</li> 650<li><strong>[Suvorova et al. (2016) <sup id="fnref:291"><a class="footnote-ref" href="#fn:291">291</a></sup> (PRD)]</strong> - Hidden Markov Model Tracking of Continuous Gravitational Waves from a Neutron Star with Wandering Spin</li> 651<li><strong>[Suvorova et al. (2017) <sup id="fnref:292"><a class="footnote-ref" href="#fn:292">292</a></sup> (PRD)]</strong> - Hidden Markov Model Tracking of Continuous Gravitational Waves from a Binary Neutron Star with Wandering Spin. II. Binary Orbital Phase Tracking</li> 652<li><strong>[Sun & Melatos (2019)] <sup id="fnref:293"><a class="footnote-ref" href="#fn:293">293</a></sup> (PRD)</strong> - Application of Hidden Markov Model Tracking to the Search for Long-Duration Transient Gravitational Waves from the Remnant of the Binary Neutron Star Merger GW170817</li> 653<li><strong>[Sun et al. (2018) <sup id="fnref:294"><a class="footnote-ref" href="#fn:294">294</a></sup> (PRD)]</strong> - Hidden Markov Model Tracking of Continuous Gravitational Waves from Young Supernova Remnants</li> 654<li><strong>[Abbott et al. (2019) <sup id="fnref:295"><a class="footnote-ref" href="#fn:295">295</a></sup> (PRD)]</strong> - Search for Gravitational Waves from Scorpius X-1 in the Second Advanced LIGO Observing Run with an Improved Hidden Markov Model<
654/li> 655<li><strong>[C. DreiÃigacker (2020) <sup id="fnref:296"><a class="footnote-ref" href="#fn:296">296</a></sup> (PhD Thesis)]</strong> - Searches for Continuous Gravitational Waves : Sensitivity Estimation and Deep Learning As a Novel Search Method</li> 656<li><strong>[Morawski et al. (2020) <sup id="fnref:297"><a class="footnote-ref" href="#fn:297">297</a></sup> (Proceedings)]</strong> - achine Learning Classification of Continuous Gravitational-wave Signal Candidates</li> 657<li><strong>[Yamamoto & Tanaka (2020) <sup id="fnref:298"><a class="footnote-ref" href="#fn:298">298</a></sup> (PRD)]</strong> - Use of Excess Power Method and Convolutional Neural Network in All-sky Search for Continuous Gravitational Waves</li> 658<li><strong>[Behechtipour & Papa <sup id="fnref:299"><a class="footnote-ref" href="#fn:299">299</a></sup> (PRD)]</strong> - Deep Learning for Clustering of Continuous Gravitational Wave Candidates II: Identification of low-SNR Candidates</li> 659<li><strong>[Beniwal et al. (2021) <sup id="fnref:300"><a class="footnote-ref" href="#fn:300">300</a></sup> (PRD)]</strong> - Search for Continuous Gravitational Waves from Ten H.E.S.S. Sources Using a Hidden Markov Model</li> 660<li><strong>[La Rosa et al. (2021) <sup id="fnref:301"><a class="footnote-ref" href="#fn:301">301</a></sup> (Universe)]</strong> - Continuous Gravitational-Wave Data Analysis with General Purpose Computing on Graphic Processing Units</li> 661<li><strong>[Melatos et al. (2021) <sup id="fnref:302"><a class="footnote-ref" href="#fn:302">302</a></sup> (PRD)]</strong> - Hidden Markov Model Tracking of Continuous Gravitational Waves from a Neutron Star with Wandering Spin. III. Rotational Phase Tracking</li> 662<li><strong>[Songsheng et al. (2021) <sup id="fnref:303"><a class="footnote-ref" href="#fn:303">303</a></sup> (ApJ)]</strong> - Search for Continuous Gravitational Wave Signals in Pulsar Timing Residuals: A New Scalable Approach with Diffusive Nested Sampling</li> 663<li><strong>[Rocha-Solache et al. (2022) [@2022Rocha-SolacheTimedomaindeeplearning] (2201.06672)]</strong> - Time-Domain Deep Learning Filtering of Structured Atmospheric Noise for Ground-Based Millimeter Astronomy</li> 664<li><strong>[Yamamoto et al. (2022) <sup id="fnref:304"><a class="footnote-ref" href="#fn:304">304</a></sup> (PRD)]</strong> - Assessing the Impact of Non-Gaussian Noise on Convolutional Neural Networks That Search for Continuous Gravitational Waves</li> 665<li><strong>[Vargas & Melatos (2022) <sup id="fnref:305"><a class="footnote-ref" href="#fn:305">305</a></sup> (2208.03932)]</strong> - Search for Continuous Gravitational Waves from PSR J0437-4715 with a Hidden Markov Model in O3 LIGO Data</li> 666<li><strong>[Jochi & Prix (2023) <sup id="fnref:306"><a class="footnote-ref" href="#fn:306">306</a></sup> (2305.01057)]</strong> - A Novel Neural-Network Architecture for Continuous Gravitational Waves</li> 667<li><strong>[Duraisamy et al. (2023) <sup id="fnref:307"><a class="footnote-ref" href="#fn:307">307</a></sup> (IEEE)]</strong> - Optimized Detection of Continuous Gravitational-Wave Signals Using Convolutional Neural Network</li> 668<li><strong>[Pintelas et al. (2023) <sup id="fnref:308"><a class="footnote-ref" href="#fn:308">308</a></sup> (Springer Nature Switzerland)]</strong> - A Deep Learning-Based Methodology for Detecting and Visualizing Continuous Gravitational Waves</li> 669<li><strong>[Dominguez et al. (2023) [@Dominguez:2023W/] (PoS)]</strong> - Convolutional Neural Network for Continuous Gravitational Waves Detection</li> 670</ul> 671<hr /> 672<h1 id="6_gravitational_wave_bursts">6. Gravitational Wave Bursts<a class="headerlink" href="#6_gravitational_wave_bursts" title="Permanent link">¶</a></h1> 673<p>A burst is a gravitational wave signal where the waveform morphology is partially or completely unknown. The source could be an unknown unknown, a supernova, cosmic string, fast radio burst, compact binaries and others. The main burst search is called coherent Wave Burst (cWB).</p> 674<blockquote></blockquote> 675<ul> 676<li> 677<p>Coherent Wave Burst (cWB) <a href="https://gwburst.gitlab.io/">Website</a></p> 678<p>
678cWB relies upon the excess coherent power in a network of detectors. The data is transformed into time-frequency domain and the clusters of time-frequency pixels above certain energy threshold are identified for each detector. Time frequency map of the single detectors is then combined using the maximisation of the likelihood over all possible sky locations and the events are then ranked according to this likelihood. We can also inform our un-modelled search about the morphology of the expected signal. cWB produces reconstructions of gravitational wave signals. It can detect CBC signals as well as bursts. <br /> 679- <strong>[Drago et al. (2020) <sup id="fnref:309"><a class="footnote-ref" href="#fn:309">309</a></sup> (2006.12604)]</strong> - Coherent Waveburst, a Pipeline for Unmodeled Gravitational-wave Data Analysis<br /> 680- <strong>[Mishra et al. (2021) <sup id="fnref:310"><a class="footnote-ref" href="#fn:310">310</a></sup> (PRD)]</strong> - Optimization of Model Independent Gravitational Wave Search Using Machine Learning<br /> 681- <strong>[Mishra et al. (2022) <sup id="fnref:311"><a class="footnote-ref" href="#fn:311">311</a></sup> (2201.01495)]</strong> - Search for Binary Black Hole Mergers in the Third Observing Run of Advanced LIGO-Virgo Using Coherent Waveburst Enhanced with Machine Learning<br /> 682- <strong>[SzczepaÅczyk et al. (2023) [@PhysRevD.107.062002] (PRD)]</strong> - Search for Gravitational-Wave Bursts in the Third Advanced LIGO-Virgo Run with Coherent WaveBurst Enhanced by Machine Learning</p> 683<blockquote></blockquote> 684</li> 685<li> 686<p>BayesWave</p> 687<p>BayesWave is another standard burst tool. <strong>[Cornish & Littenberg (2015) <sup id="fnref:312"><a class="footnote-ref" href="#fn:312">312</a></sup> (CQG)]</strong> Models signals as a variable number of sine-Gaussian wavelets with power coherent across detectors. It produces unmodelled waveform reconstructions and can remove glitches that occur during signals. <strong>[Pankow et al. (2018) <sup id="fnref:313"><a class="footnote-ref" href="#fn:313">313</a></sup> (PRD)]</strong></p> 688<blockquote></blockquote> 689</li> 690<li> 691<p>Supernova Search</p> 692<p>Some burst searches are for targeted sources like supernovae. There is not enough supernova waveforms to match filter search but some supernova waveform features are known. The known features from supernova simulations can be incorporated into supernova searches using machine learning.</p> 693<ul> 694<li><strong>[Astone et al. (2018) <sup id="fnref:42"><a class="footnote-ref" href="#fn:42">42</a></sup> (PRD)]</strong> enhance the efficiency of cWB using a neural network. The network is trained on phenomenological waveforms that represent the g-mode emission in supernova waveforms. They use cWB to prepare images of the data. They use colours to determine which detectors find the signal. They find their method increases the sensitivity of traditional cWB.</li> 695<li><strong>[Iess et al. (2020) <sup id="fnref:314"><a class="footnote-ref" href="#fn:314">314</a></sup> (Mach. learn.: sci. technol.)]</strong> have a different approach that does not involve cWB. They use a trigger generator called WDF to find excess power in the detector. Then they do a neural network classification to decide if the trigger is a signal or noise. They train directly on supernova waveforms. They use both time series and images of data. They obtain high accuracies with both methods and include glitches.</li> 696<li><strong>[Chan et al. (2019) <sup id="fnref:315"><a class="footnote-ref" href="#fn:315">315</a></sup> (PRD)]</strong> also train directly on supernova waveforms. They use only the time series waveforms from different explosion mechanisms.</li> 697<li><strong>[Cavaglia et al. (2020) [@Cavaglia2020qzp] (Mach. learn.: sci. technol.)]</strong> - Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach</li> 698<li><strong>[Stachie et al. (2020) <sup id="fnref:316"><a class="footnote-ref" href="#fn:316">316</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - Using Machine Learning for Transient Classification in Searches for Gravitational-wave Counterparts</li> 699<li><strong>[Marianer et al. (2020) <sup id="fnref:49"><a class="footnote-ref" href="#fn:49">49</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - A Semisupervised Machine Learning Search for Never-Seen Gravitational-Wave Sources</li> 700<li><strong>[Millhouse et al. (2020) <sup id="fnref:317"><a class="footnote-ref" href="#fn:317">317</a></sup> (PRD)]</strong> - Search for Gravitational Waves from 12 Young Supernova Remnants with a Hidden Markov Model in Advanced LIGO’s Second Observing Run</li> 701<li><strong>[L'opez et al. (2021) <sup id="fnref2:318"><a class="footnote-ref" href="#fn:318">318</a></sup> (PRD)]</strong> - Deep Learning for Core-collapse Supernova Detection</li> 702<li><strong>[L'operz et al. (2021) <sup id="fnref:319"><a class="footnote-ref" href="#fn:319">319</a></sup> (IEEE)]</strong> - Deep Learning Algorithms for Gravitational Waves Core-collapse Supernova Detection</li> 703<li><strong>[Antelis et al. (2021) <sup id="fnref:320"><a class="footnote-ref" href="#fn:320">320</a></sup> (PRD)]</strong> - Using Supervised Learning Algorithms As a Follow-up Method in the Search of Gravitational Waves from C
703ore-collapse Supernovae</li> 704<li><strong>[Lagos et al. (2023) <sup id="fnref:321"><a class="footnote-ref" href="#fn:321">321</a></sup> (2304.11498)]</strong> - Characterizing the Gravitational Wave Temporal Evolution of the Gmode Fundamental Resonant Frequency for a Core Collapse Supernova: A Neural Network Approach<blockquote></blockquote> 705</li> 706</ul> 707</li> 708<li> 709<p>Burst analysis</p> 710<p>Jordan McGinn. Thesis: “Generalised gravitational burst searches with Generative Adversarial Networks”. Examined the use of Generative Adversarial Networks to generate and interpret large quantities of time-series data. The method was successful in classifying signal data buried within external noise. </p> 711<p>Uses Generative Adversarial network (GAN) to learn how to make standard burst waveforms. Generation stage has the possibility to make signals spanning all training classes. Discriminator stage has the potential to be a general transient detection tool</p> 712<ul> 713<li><strong>[Rover et al. (2009) <sup id="fnref:322"><a class="footnote-ref" href="#fn:322">322</a></sup> (PRD)]</strong> - Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce</li> 714<li><strong>[KovaÄeviÄ et al. (2019) [#Kovacevic2019wpy] (Mon. Not. Roy. Astron. Soc.)]</strong> - Optimizing neural network techniques in classifying Fermi-LAT gamma-ray
714sources</li> 715<li><strong>[Kim et al. (2015) <sup id="fnref:323"><a class="footnote-ref" href="#fn:323">323</a></sup> (CQG)]</strong> - Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts</li> 716<li><strong>[Gayathri et al. (2020) <sup id="fnref:324"><a class="footnote-ref" href="#fn:324">324</a></sup> (PRD)]</strong> - Enhancing the Sensitivity of Transient Gravitational Wave Searches with Gaussian Mixture Models</li> 717<li><strong>[L'opez et al. (2021) <sup id="fnref:318"><a class="footnote-ref" href="#fn:318">318</a></sup> (PRD)]</strong> - Deep Learning for Core-collapse Supernova Detection</li> 718<li><strong>[Skliris et al. (2020) [@2020SklirisRealtimeDetection] (2009.14611)]</strong> - Real-time Detection of Unmodeled Gravitational-wave Transients Using Convolutional Neural Networks</li> 719<li><strong>[Lopez et al. (2021) <sup id="fnref:325"><a class="footnote-ref" href="#fn:325">325</a></sup> (PRD)]</strong> - Utilizing Gaussian Mixture Models in All-Sky Searches for Short-Duration Gravitational Wave Bursts</li> 720<li><strong>[Boudart & Fays (2022) <sup id="fnref:326"><a class="footnote-ref" href="#fn:326">326</a></sup> (PRD)]</strong> - A Machine Learning Algorithm for Minute-Long Burst Searches</li> 721<li><strong>[Mitra et al. (2022) <sup id="fnref:327"><a class="footnote-ref" href="#fn:327">327</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - Exploring Supernova Gravitational Waves with Machine Learning</li> 722<li><strong>[SzczepaÅczyk et al. (2022) <sup id="fnref:328"><a class="footnote-ref" href="#fn:328">328</a></sup> (2210.01754)]</strong> - All-Sky Search for Gravitational-Wave Bursts in the Third Advanced LIGO-Virgo Run with Coherent WaveBurst Enhanced by Machine Learning</li> 723<li><strong>[Iess et al. (2022) <sup id="fnref:329"><a class="footnote-ref" href="#fn:329">329</a></sup> (A&A)]</strong> - LSTM and CNN Application for Core-Collapse Supernova Search in Gravitational Wave Real Data</li> 724<li><strong>[Boudart & Fays (2022) <sup id="fnref:330"><a class="footnote-ref" href="#fn:330">330</a></sup> (IEEE)]</strong> - ALBUS: A Machine Learning Algorithm for Gravitational Wave Burst Searches</li> 725<li><strong>[Modafferi et al. (2022) <sup id="fnref:331"><a class="footnote-ref" href="#fn:331">331</a></sup> (2303.16720)]</strong> - Convolutional Neural Network Search for Long-Duration Transient Gravitational Waves from Glitching Pulsars</li> 726<li><strong>[Sasaoka et al. (2023) [@Sasaoka:20232F] (PoS)]</strong> - Deep Learning for Detecting Gravitational Waves from Compact Binary Coalescences and Its Visualization by Grad-CAM</li> 727<li><strong>[Meijer et al. (2023) <sup id="fnref:332"><a class="footnote-ref" href="#fn:332">332</a></sup> (2308.12323)]</strong> - Gravitational-Wave Searches for Cosmic String Cusps in Einstein Telescope Data Using Deep Learning</li> 728</ul> 729</li> 730</ul> 731<blockquote></blockquote> 732<ul> 733<li> 734<p>Single Detector Search</p> 735<p>30% of gravitational wave data is collected when only 1 detector is in observing mode. Canât do time slides to measure the background if there is only 1 detector. </p> 736<ul> 737<li><strong>[Cavaglia et al. (2020) [@Cavaglia2020qzp] (Mach. learn.: sci. technol.)]</strong> use machine learning combined with cWB to perform a single detector search for supernovae. They train a genetic programming algorithm on the output parameters of cWB.</li> 738<li>(How can I try it myself?) You can download some supernova gravitational wave signals <a href="http://www.phys.utk.edu/smc/data.html">here</a> . You can get KarooGP <a href="http://kstaats.github.io/karoo_gp/">here </a>. You can get Coherent WaveBurst <a href="https://gwburst.gitlab.io/">here</a>.</li> 739</ul> 740</li> 741</ul> 742<hr /> 743<h1 id="7_stochastic_gravitational_wave_background">
7437. Stochastic Gravitational Wave Background<a class="headerlink" href="#7_stochastic_gravitational_wave_background" title="Permanent link">¶</a></h1> 744<ul> 745<li><strong>[Utina et al. (2021) <sup id="fnref:333"><a class="footnote-ref" href="#fn:333">333</a></sup> (IEEE)]</strong> - Deep Learning Searches for Gravitational Wave Stochastic Backgrounds</li> 746<li><strong>[Yamamoto et al. (2022) <sup id="fnref:334"><a class="footnote-ref" href="#fn:334">334</a></sup> (PRD)]</strong> - Deep Learning for Intermittent Gravitational Wave Signals</li> 747</ul> 748<hr /> 749<h1 id="8_gw_cosmology">8. GW / Cosmology<a class="headerlink" href="#8_gw_cosmology" title="Permanent link">¶</a></h1> 750<ul> 751<li><strong>[Khan et al. (2019) <sup id="fnref:335"><a class="footnote-ref" href="#fn:335">335</a></sup> (PLB)]</strong> From the citizen science revolution using the Sloan Digital Sky Survey⦠… to large scale discovery using unlabeled images in the Dark Energy Survey using deep learning. 10k+ raw, unlabeled galaxy images from DES clustered according to morphology using RGB filters; Scalable approach to curate datasets, and to construct large-scale galaxy catalogs; Deep transfer learning combined with distributed training for cosmology; Training is completed within 8 minutes achieving state-of-the-art classification accuracy; </li> 752<li>Real-time detection and characterization of binary black hole mergers + Classification and regression of galaxies across redshift in DES/LSST-type surveys => Hubble constant measurements with probabilistic neural network models <strong>[Wei et al. (2020) <sup id="fnref:336"><a class="footnote-ref" href="#fn:336">336</a></sup> (Mon. Not. Roy. Astron. Soc.)].</strong> Star cluster classification has been predominantly done by human experts; We have designed neural network models that outperform, for the first time, human performance for star cluster classification; Worldwide collaboration of experts in deep learning, astronomy, software and data.</li> 753<li><strong>[Alexander et al. (2020) <sup id="fnref:337"><a class="footnote-ref" href="#fn:337">337</a></sup> (ApJ)]</strong> - Deep Learning the Morphology of Dark Matter Substructure</li> 754<li><strong>[Gupta & Reichardt (2020) <sup id="fnref:338"><a class="footnote-ref" href="#fn:338">338</a></sup> (ApJ)]</strong> - Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel’dovich Effect</li> 755<li><strong>[Sadr & Farsian (2020) <sup id="fnref:339"><a class="footnote-ref" href="#fn:339">339</a></sup> (JCAP)]</strong> - Inpainting via Generative Adversarial Networks for CMB data analysis</li> 756<li><strong>[Philip et al. (2002) <sup id="fnref:340"><a class="footnote-ref" href="#fn:340">340</a></sup> (Astron. Astrophys.)]</strong> - A difference boosting neural network for automated star-galaxy classification</li> 757<li><strong>[Philip et al. (2012) <sup id="fnref:341"><a class="footnote-ref" href="#fn:341">341</a></sup> (1211.3607)]</strong> - Classification by Boosting Differences in Input Vectors: An application to datasets from Astronomy</li> 758<li><strong>[Sadeh (2020) <sup id="fnref:342"><a class="footnote-ref" href="#fn:342">342</a></sup> (ApJ)]</strong> - Data-driven Detection of Multimessenger Transients</li> 759<li><strong>[Wang et al (2020) <sup id="fnref:343"><a class="footnote-ref" href="#fn:343">343</a></sup> (ApJS)]</strong> - ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks<ul> 760<li>ä»ä»¬è¿ä¸ªæ¯å½ååå½é®é¢å¨åï¼å Huerta ä»ä»¬çåºæ¬é»è¾å ¶å®ä¸æ ·ãè³äº6 ä¸ªç®æ åæ°çä¼°è®¡ï¼æ¯éè¿ç»å® input æ°æ®å¨ç¸åºåæ°åºé´ä¸éæ ·åï¼ç´æ¥ç»åºåéªæ ·æ¬åæ°ä¼°è®¡çï¼é¢ç妿´¾~ï¼ã并䏿¯å¯¹æåä¸ªæ°æ®ç»åºçåæ°ä¼°è®¡ï¼è´å¶æ¯å¦æ´¾ï¼ã</li> 761<li>å®ç°çæ¯å¯¹åºè§æµæ°æ®éçå®å®å¦åæ°ä¼°è®¡</li> 762</ul> 763</li> 764<li><strong>[Xu et al. (2020) <sup id="fnref:344"><a class="footnote-ref" href="#fn:344">344</a></sup> (PASP)]</strong> - GWOPS: A Vo-technology Driven Tool to Search for the Electromagnetic Counterpart of Gravitational Wave Event</li> 765<li><strong>[Milosevic et al. (2020) <sup id="fnref:345"><a class="footnote-ref" href="#fn:345">345</a></sup> (A&A)]</strong> - Bayesian Decomposition of the Galactic Multi-frequency Sky Using Probabilistic Autoencoders</li> 766<li><strong>[Hortua et al. (2019) <sup id="fnref:346"><a class="footnote-ref" href="#fn:346">346</a></sup> (PRD)]</strong>
766 - Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks</li> 767<li><strong>[Matilla et al. (2020) <sup id="fnref:347"><a class="footnote-ref" href="#fn:347">347</a></sup> (PRD)]</strong> - Interpreting Deep Learning Models for Weak Lensing</li> 768<li><strong>[Guzman & Meyers (2021) <sup id="fnref:348"><a class="footnote-ref" href="#fn:348">348</a></sup> (PRD))]</strong> - Reconstructing Patchy Reionization with Deep Learning</li> 769<li><strong>[Boilla et al. (2021) <sup id="fnref:349"><a class="footnote-ref" href="#fn:349">349</a></sup> (2102.06149)]</strong> - Reconstruction of the Dark Sectors’ Interaction: A Model-independent Inference and Forecast from GW Standard Sirens</li> 770<li><strong>[Ren et al. (2021) <sup id="fnref:350"><a class="footnote-ref" href="#fn:350">350</a></sup> (2103.01260)]</strong> - Data-driven Reconstruction of the Late-time Cosmic Acceleration with F(t) Gravity</li> 771<li><strong>[Yang (2021) <sup id="fnref:351"><a class="footnote-ref" href="#fn:351">351</a></sup> (2103.01923)]</strong> - Gravitational-wave Detector Networks: Standard Sirens on Cosmology and Modified Gravity Theory</li> 772<li><strong>[Han et al. (2021) <sup id="fnref:352"><a class="footnote-ref" href="#fn:352">352</a></sup> (ApJ)]</strong> - Bayesian Nonparametric Inference of Neutron Star Equation of State Via Neural Network</li> 773<li><strong>[Edwards (2020) <sup id="fnref:353"><a class="footnote-ref" href="#fn:353">353</a></sup> (PRD)]</strong> - Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning</li> 774<li><strong>[Natarajan et al. (2021) <sup id="fnref:354"><a class="footnote-ref" href="#fn:354">354</a></sup> (2103.13932)]</strong> - Quasarnet: A New Research Platform for the Data-driven Investigation of Black Holes</li> 775<li><strong>[Elizalde et al. (2021) <sup id="fnref:355"><a class="footnote-ref" href="#fn:355">355</a></sup> (2104.01077)]</strong> - An Approach to Cold Dark Matter Deviation and the <span><span class="MathJax_Preview">H_{0}</span>
775<script type="math/tex">H_{0}</script>
775</span> Tension Problem by Using Machine Learning</li> 776<li><strong>[Gómez-Vargas et al. (2021) <sup id="fnref:356"><a class="footnote-ref" href="#fn:356">356</a></sup> (2104.00595)]</strong> - Cosmological Reconstructions with Artificial Neural Networks</li> 777<li><strong>[Tilaver et al. (2021) <sup id="fnref:357"><a class="footnote-ref" href="#fn:357">357</a></sup> (Comput. Phys. Commun)]</strong> - Deep Learning Approach to Hubble Parameter</li> 778<li><strong>[Gerardi et al. (2021) <sup id="fnref:358"><a class="footnote-ref" href="#fn:358">358</a></sup> (PRD)]</strong> - Unbiased Likelihood-free Inference of the Hubble Constant from Light Standard Sirens</li> 779<li><strong>[Velasquez-Toribio et al. (2021) <sup id="fnref:359"><a class="footnote-ref" href="#fn:359">359</a></sup> (2104.07356)]</strong> - Constraints on Cosmographic Functions Using Gaussian Processes</li> 780<li><strong>[Cañas-Herrera et al. (2021) <sup id="fnref:360"><a class="footnote-ref" href="#fn:360">360</a></sup> (ApJ)]</strong> - Learning How to Surf: Reconstructing the Propagation and Origin of Gravitational Waves with Gaussian Processes</li> 781<li><strong>[Rouhiainen et al. (2021) <sup id="fnref:361"><a class="footnote-ref" href="#fn:361">361</a></sup> (2105.12024)]</strong> - Normalizing Flows for Random Fields in Cosmology</li> 782<li><strong>[Mancarella et al. (2022) <sup id="fnref:362"><a class="footnote-ref" href="#fn:362">362</a></sup> (PRD)]</strong> - Seeking New Physics in Cosmology with Bayesian Neural Networks: Dark Energy and Modified Gravity</li> 783<li><strong>[Chapman-Bird et al. (2022) [@2022Chapman-BirdRapiddeterminationLISA] (2212.06166)]</strong> - Rapid Determination of LISA Sensitivity to Extreme Mass Ratio Inspirals with Machine Learning</li> 784<li><strong>[Gagnon-Hartman et al. (2023) [@2023Gagnon-HartmanDebiasingStandardSiren] (2301.05241)]</strong> Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation</li> 785<li><strong>[Shah et al. (2023) <sup id="fnref:363"><a class="footnote-ref" href="#fn:363">363</a></sup> (2301.12708)]</strong> - A Thorough Investigation of the Prospects of eLISA in Addressing the Hubble Tension: Fisher Forecast, MCMC and {{Machine Learning</li> 786<li><strong>[Ashton (2023) <sup id="fnref:364"><a class="footnote-ref" href="#fn:364">364</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - Gaussian Processes for Glitch-robust Gravitational-wave Astronomy</li> 787<li><strong>[Callister &
787 Farr (2023) <sup id="fnref:365"><a class="footnote-ref" href="#fn:365">365</a></sup> (2302.07289)]</strong> - A Parameter-Free Tour of the Binary Black Hole Population</li> 788<li><strong>[Riley & Mandel (2023) <sup id="fnref:366"><a class="footnote-ref" href="#fn:366">366</a></sup> (2303.00508)]</strong> - Probing Cosmic History with Merging Compact Binaries</li> 789<li><strong>[Mukherjee et al. (2023) <sup id="fnref:367"><a class="footnote-ref" href="#fn:367">367</a></sup> (2303.05169)]</strong> - Reconstructing the Hubble Parameter with Future Gravitational Wave Missions Using Machine Learning</li> 790<li><strong>[Alvey et al. (2023) <sup id="fnref:368"><a class="footnote-ref" href="#fn:368">368</a></sup> (2304.02032)]</strong> - Albatross: A Scalable Simulation-Based Inference Pipeline for Analysing Stellar Streams in the Milky Way</li> 791<li><strong>[Whittle et al. (2023) <sup id="fnref:369"><a class="footnote-ref" href="#fn:369">369</a></sup> (2305.13780)]</strong> - Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories</li> 792<li><strong>[Sravan et al. (2023) <sup id="fnref:370"><a class="footnote-ref" href="#fn:370">370</a></sup> (2307.09213)]</strong> - Machine-Directed Gravitational-Wave Counterpart Discovery</li> 793</ul> 794<h1 id="9_physics_related">9. Physics related<a class="headerlink" href="#9_physics_related" title="Permanent link">¶</a></h1> 795<p>Some selected interesting works:</p> 796<ul> 797<li><strong>[Funai et al. (2018) <sup id="fnref:371"><a class="footnote-ref" href="#fn:371">371</a></sup> (PRR)]</strong> - Thermodynamics and Feature Extraction by Machine Learning</li> 798<li><strong>[Breen et al. (2019) <sup id="fnref:372"><a class="footnote-ref" href="#fn:372">372</a></sup> (Mon. Not. Roy. Astron. Soc.)]</strong> - Newton Vs the Machine: Solving the Chaotic Three-body Problem Using Deep Neural Networks | <a href="https://mp.weixin.qq.com/s?__biz=MzA5ODEzMjIyMA==&mid=2247495819&idx=3&sn=61d214da5c7d6e6b7dac1c4ba7b1b3b7&source=41#wechat_redirect">深度å¦ä¹ æ±è§£ãä¸ä½ãé®é¢ï¼è®¡ç®é度æé«ä¸äº¿å</a> | <a href="https://mp.weixin.qq.com/s/zdj7Lcud51u0Grg3YWUUkg">çé¡¿è§£å³ä¸äºçé®é¢ï¼AIæè®¸è½æå®ï¼ç¨ç¥ç»ç½ç»è§£å³ä¸ä½é®é¢</a></li> 799<li><strong>[Greydanus et al. (2019) <sup id="fnref:373"><a class="footnote-ref" href="#fn:373">373</a></sup> (NeurIPS)]</strong> - Hamiltonian Neural Networks</li> 800<li><strong>[Cohen et al. (2019) <sup id="fnref:374"><a class="footnote-ref" href="#fn:374">374</a></sup>ï¼PRR)]</strong> - Learning Curves for Overparametrized Deep Neural Networks: A Field Theory Perspective</li> 801<li><strong>[Rosofsky & Huerta (2020) <sup id="fnref:375"><a class="footnote-ref" href="#fn:375">375</a></sup> (PRD)]</strong> - Artificial Neural Network Subgrid Models of 2D Compressible Magnetohydrodynamic Turbulence</li> 802<li><strong>[Tamayo et al. (2020) <sup id="fnref:376"><a class="footnote-ref" href="#fn:376">376</a></sup> (PNAS)]</strong> - Predicting the Long-term Stability of Compact Multiplanet Systems<blockquote> 803<p>Saganå¦è <a href="https://twitter.com/astrodantamayo/status/1282866485531222022?s=20">Dan Tamayo</a>ä»ç»äºä»ä»¬å¨PNASä¸å表çä¸ç¯å©ç¨æºå¨å¦ä¹ ææ¯é¢æµå¤è¡æç³»ç»çå¨åå¦ç¨³å®æ§ã(Informative <a href="http://weibointl.api.weibo.com/share/159645756.html?weibo_id=4526731371732253">comments</a> from å 头æªå士)</p> 804</blockquote> 805</li> 806<li><strong>[Green & Ting (2020) <sup id="fnref:377"><a class="footnote-ref" href="#fn:377">377</a></sup> (2011.04673)]</strong> - Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space</li> 807<li><strong>[Liu & Tegmark (2021) <sup id="fnref:378"><a class="footnote-ref" href="#fn:378">378</a></sup> (PRL)]</strong> - Machine Learning Conservation Laws from Trajectories</li> 808<li><strong>[Lucie-Smith et al. (2020) <sup id="fnref:379"><a class="footnote-ref" href="#fn:379">379</a></sup> (2011.10577)]</strong> - Deep Learning Insights into Cosmological Structure Formation</li> 809<li><strong>[Yip et al. (2020) <sup id="fnref:380"><a class="footnote-ref" href="#fn:380">380</a></sup> (ApJ)]</strong> - Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals</li> 810<li><strong>[Glüsenkamp (2020)<sup id="fnref:381"><a class="footnote-ref" href="#fn:381">381</a></sup> (2008.05825)]</strong> - Unifying Supervised Learning and VAEs – Automating Statistical Inference in High-energy Physics</li> 811<li><strong>[Rousseau et al. (2020) <sup id="fnref:382"><a class="footnote-ref" href="#fn:382">382</a></sup> (AIHEP)]</strong> - Machine Learning Scientific Competitions and Datasets</li> 812<li><strong>[Cranmer et al. (2021) <sup id="fnref:383"><a class="footnote-ref" href="#fn:383">383</a></sup> (PNAS)]</strong> - A Bayesian Neural Network Predicts the Dissolution of Compact Planetary Systems</li> 813<li><strong>
813[Kochkov et al. (2021) <sup id="fnref:384"><a class="footnote-ref" href="#fn:384">384</a></sup> (PANS)]</strong> - Machine Learning Accelerated Computational Fluid Dynamics<blockquote> 814<p><a href="https://mp.weixin.qq.com/s/QmlYIIcG7pjzLfLmFdDnDQ">æºå¨å¦ä¹ 䏿µä½å¨åå¦ï¼è°·æAIå©ç¨ãML+TPUãå®ç°æµä½æ¨¡ææ°é级å é</a></p> 815</blockquote> 816</li> 817<li><strong>[Visschers et al. (2021) <sup id="fnref:385"><a class="footnote-ref" href="#fn:385">385</a></sup> (Mach. learn.: sci. technol.)]</strong> - Rapid Parameter Estimation of Discrete Decaying Signals Using Autoencoder Networks</li> 818<li><strong>[Guidetti et al. (2021) <sup id="fnref:386"><a class="footnote-ref" href="#fn:386">386</a></sup> (2103.08662)]</strong> - dNNsolve: an efficient NN-based PDE solver</li> 819<li><strong>[Liu et al. (2021) <sup id="fnref:387"><a class="footnote-ref" href="#fn:387">387</a></sup> (PRE)]</strong> - Machine-learning Non-conservative Dynamics for New-physics Detection</li> 820<li><strong>[Rosofsky & Huerta (2022) <sup id="fnref:388"><a class="footnote-ref" href="#fn:388">388</a></sup> (Mach. learn.: sci. technol.)]</strong> - Applications of Physics Informed Neural Operators</li> 821<li><strong>[Katsube et al. (2022) <sup id="fnref:389"><a class="footnote-ref" href="#fn:389">389</a></sup> (PRD)]</strong> - Deep Learning Metric Detectors in General Relativity</li> 822<li><strong>[Luna et al. (2022) <sup id="fnref:390"><a class="footnote-ref" href="#fn:390">390</a></sup> (2212.06103)]</strong> - Solving the Teukolsky Equation with Physics-Informed Neural Networks</li> 823<li><strong>[Cornell et al. (2022) [@PhysRevD.106.124047] ï¼PRDï¼]</strong> - Using Physics-Informed Neural Networks to Compute Quasinormal Modes</li> 824<li><strong>[Hatefi et al. (2023) <sup id="fnref:391"><a class="footnote-ref" href="#fn:391">391</a></sup> (2302.04619)]</strong> - Analysis of Black Hole Solutions in Parabolic Class Using Neural Networks</li> 825<li><strong>[Sabbatini & Grimani (2023) <sup id="fnref:392"><a class="footnote-ref" href="#fn:392">392</a></sup> (2302.06740)]</strong> - Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA</li> 826<li><strong>[Ma & Vajente (2023) <sup id="fnref:393"><a class="footnote-ref" href="#fn:393">393</a></sup> (2302.07921)]</strong> - A Deep Learning Technique to Control the Non-linear Dynamics of a Gravitational-wave Interferometer</li> 827<li><strong>[Rosofsky & Huerta (2023) <sup id="fnref:394"><a class="footnote-ref" href="#fn:394">394</a></sup> (2302.08332)]</strong> - Magnetohydrodynamics with Physics Informed Neural Operators</li> 828<li><strong>[Lim et al. (2023) <sup id="fnref:395"><a class="footnote-ref" href="#fn:395">395</a></sup> (2305.13358)]</strong> - Mapping Dark Matter in the Milky Way Using Normalizing Flows and Gaia DR3</li> 829<li><strong>[Dialektopoulos et al. (2023) <sup id="fnref:396"><a class="footnote-ref" href="#fn:396">396</a></sup> (2305.15500)]</strong> - Neural Network Reconstruction of Scalar-Tensor Cosmology</li> 830<li><strong>[Liu & Max (2022) <sup id="fnref:397"><a class="footnote-ref" href="#fn:397">397</a></sup> (PRL)]</strong> - Machine-Learning Hidden Symmetries</li> 831</ul> 832<h1 id="license">License<a class="headerlink" href="#license" title="Permanent link">¶</a></h1> 833<ul> 834<li><a rel="license" href="http://creativecommons.org/licenses/by-nc/3.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/3.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/3.0/">Creative Commons Attribution-NonCommercial 3.0 Unported License</a>.</li> 835</ul>
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1106</span>:033015, October 2019. <a href="https://arxiv.org/abs/1905.09300">arXiv:1905.09300</a>, <a href="https://doi.org/10.1103/PhysRevResearch.1.033015">doi:10.1103/PhysRevResearch.1.033015</a>. <a class="footnote-backref" href="#fnref:89" title="Jump back to footnote 89 in the text">↩</a></p> 1107</li> 1108<li id="fn:90"> 1109<p>Alvin J.K. Chua. <em>Topics in Gravitational-Wave Astronomy: Theoretical Studies, Source Modelling and Statistical Methods</em>. PhD thesis, Cambridge U., Inst. of Astron., May 2017. <a href="https://doi.org/10.17863/CAM.9010">doi:10.17863/CAM.9010</a>. <a class="footnote-backref" href="#fnref:90" title="Jump back to footnote 90 in the text">↩</a></p> 1110</li> 1111<li id="fn:91"> 1112<p>Alvin J. K. Chua, Chad R. Galley, and Michele Vallisneri. Reduced-order modeling with artificial neurons for gravitational-wave inference. <em>Physical Review Letters</em>, 122<span><span class="MathJax_Preview">21</span>
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1112</span>:211101, May 2019. <a href="https://arxiv.org/abs/1811.05491">arXiv:1811.05491</a>, <a href="https://doi.org/10.1103/PhysRevLett.122.211101">doi:10.1103/PhysRevLett.122.211101</a>. <a class="footnote-backref" href="#fnref:91" title="Jump back to footnote 91 in the text">↩</a></p> 1113</li> 1114<li id="fn:92"> 1115<p>Stefano Schmidt. Gravitational wave modelling with machine lerning. Master’s thesis, Università degli Studi di Milano, 2019. <a class="footnote-backref" href="#fnref:92" title="Jump back to footnote 92 in the text">↩</a></p> 1116</li> 1117<li id="fn:93"> 1118<p>Zhuo Chen, E. A. Huerta, Joseph Adamo, Roland Haas, Eamonn O’Shea, Prayush Kumar, and Chris Moore. Observation of eccentric binary black hole mergers with second and third generation gravitational wave detector networks. <em>Physical Review D</em>, 103<span><span class="MathJax_Preview">8</span>
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1118</span>:084018, April 2021. <a href="https://arxiv.org/abs/2008.03313">arXiv:2008.03313</a>, <a href="https://doi.org/10.1103/PhysRevD.103.084018">doi:10.1103/PhysRevD.103.084018</a>. <a class="footnote-backref" href="#fnref:93" title="Jump back to footnote 93 in the text">↩</a></p> 1119</li> 1120<li id="fn:94"> 1121<p>Sebastian Khan and Rhys Green. Gravitational-wave surrogate models powered by artificial neural networks. <em>Physical Review D</em>, 103<span><span class="MathJax_Preview">6</span>
1121<script type="math/tex">6</script>
1121</span>:064015, March 2021. <a href="https://arxiv.org/abs/2008.12932v1">arXiv:2008.12932v1</a>, <a href="https://doi.org/10.1103/PhysRevD.103.064015">doi:10.1103/PhysRevD.103.064015</a>. <a class="footnote-backref" href="#fnref:94" title="Jump back to footnote 94 in the text">↩</a></p> 1122</li> 1123<li id="fn:95"> 1124<p>Stefano Schmidt, Matteo Breschi, Rossella Gamba, Giulia Pagano, Piero Rettegno, Gunnar Riemenschneider, Sebastiano Bernuzzi, Alessandro Nagar, and Walter Del Pozzo. Machine learning gravitational waves from binary black hole mergers. <em>Physical Review D</em>, 103:043020, February 2021. <a href="https://arxiv.org/abs/2011.01958">arXiv:2011.01958</a>. <a class="footnote-backref" href="#fnref:95" title="Jump back to footnote 95 in the text">↩</a></p> 1125</li> 1126<li id="fn:96"> 1127<p>Joongoo Lee, Sang Hoon Oh, Kyungmin Kim, Gihyuk Cho, John J. Oh, Edwin J. Son, and Hyung Mok Lee. Deep learning model on gravitational waveforms in merging and ringdown phases of binary black hole coalescences. <em>Physical Review D</em>, 103<span><span class="MathJax_Preview">12</span>
1127<script type="math/tex">12</script>
1127</span>:123023, June 2021. <a href="https://arxiv.org/abs/2101.05685">arXiv:2101.05685</a>, <a href="https://doi.org/10.1103/PhysRevD.103.123023">doi:10.1103/PhysRevD.103.123023</a>. <a class="footnote-backref" href="#fnref:96" title="Jump back to footnote 96 in the text">↩</a></p> 1128</li> 1129<li id="fn:97"> 1130<p>Chung-Hao Liao and Feng-Li Lin. Deep generative models of gravitational waveforms via conditional autoencoder. <em>Physical Review D</em>, 103<span><span class="MathJax_Preview">12</span>
1130<script type="math/tex">12</script>
1130</span>:124051, June 2021. <a href="https://arxiv.org/abs/2101.06685">arXiv:2101.06685</a>, <a href="https://doi.org/10.1103/PhysRevD.103.124051">doi:10.1103/PhysRevD.103.124051</a>. <a class="footnote-backref" href="#fnref:97" title="Jump back to footnote 97 in the text">↩</a></p> 1131</li> 1132<li id="fn:98"> 1133<p>Alvin J. K. Chua, Michael L. Katz, Niels Warburton, and Scott A. Hughes. Rapid generation of fully relativistic extreme-mass-ratio-inspiral waveform templates for LISA data analysis. <em>Physical Review Letters</em>, 126<span><span class="MathJax_Preview">5</span>
1133<script type="math/tex">5</script>
1133</span>:051102, February 2021. <a href="https://arxiv.org/abs/2008.06071">arXiv:2008.06071</a>, <a href="https://doi.org/10.1103/PhysRevLett.126.051102">doi:10.1103/PhysRevLett.126.051102</a>. <a class="footnote-backref" href="#fnref:98" title="Jump back to footnote 98 in the text">↩</a></p> 1134</li> 1135<li id="fn:99"> 1136<p>Brendan Keith, Akshay Khadse, and Scott E. Field. Learning orbital dynamics of binary black hole systems from gravitational wave measurements. <em>Physical Review Research</em>, 3<span><span class="MathJax_Preview">4</span>
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1136</span>:043101, November 2021. <a href="https://arxiv.org/abs/2102.12695">arXiv:2102.12695</a>, <a href="https://doi.org/10.1103/PhysRevResearch.3.043101">doi:10.1103/PhysRevResearch.3.043101</a>. <a class="footnote-backref" href="#fnref:99" title="Jump back to footnote 99 in the text">↩</a></p> 1137</li> 1138<li id="fn:100"> 1139<p>J McGinn, C Messenger, M J Williams, and I S Heng. Generalised gravitational wave burst generation with generative adversarial networks. <em>Classical and Quantum Gravity</em>, 38<span><span class="MathJax_Preview">15</span>
1139<script type="math/tex">15</script>
1139</span>:155005, June 2021. <a href="https://arxiv.org/abs/2103.01641">arXiv:2103.01641</a>, <a href="https://doi.org/10.1088/1361-6382/ac09cc">doi:10.1088/1361-6382/ac09cc</a>. <a class="footnote-backref" href="#fnref:100" title="Jump back to footnote 100 in the text">↩</a></p> 1140</li> 1141<li id="fn:101"> 1142<p>Paraskevi Nousi, Styliani-Christina Fragkouli, Nikolaos Passalis, Panagiotis Iosif, Theocharis Apostolatos, George Pappas, Nikolaos Stergioulas, and Anastasios Tefas. Autoencoder-driven spiral representation learning for gravitational wave surrogate modelling. <em>Neurocomputing</em>, 491:67â77, June 2022. <a href="https://arxiv.org/abs/2107.04312">arXiv:2107.04312</a>, <a href="https://doi.org/10.1016/j.neucom.2022.03.052">doi:10.1016/j.neucom.2022.03.052</a>. <a class="footnote-backref" href="#fnref:101" title="Jump back to footnote 101 in the text">↩</a></p> 1143</li> 1144<li id="fn:102"> 1145<p>Damián Barsotti, Franco Cerino, Manuel Tiglio, and Aarón Villanueva. Gravitational wave surrogates through automated machine learning. <em>Classical and Quantum Gravity</em>, 39<span><span class="MathJax_Preview">8</span>
1145<script type="math/tex">8</script>
1145</span>:085011, March 2022. <a href="https://arxiv.org/abs/2110.08901">arXiv:2110.08901</a>, <a href="https://doi.org/10.1088/1361-6382/ac5ba1">doi:10.1088/1361-6382/ac5ba1</a>. <a class="footnote-backref" href="#fnref:102" title="Jump back to footnote 102 in the text">↩</a></p> 1146</li> 1147<li id="fn:103"> 1148<p>Asad Khan, E. A. Huerta, and Huihuo Zheng. Interpretable AI forecasting for numerical relativity waveforms of quasicircular, spinning, nonprecessing binary black hole mergers. <em>Physical Review D</em>, 105<span><span class="MathJax_Preview">2</span>
1148<script type="math/tex">2</script>
1148</span>:024024, January 2022. <a href="https://arxiv.org/abs/2110.06968">arXiv:2110.06968</a>, <a href="https://doi.org/10.1103/PhysRevD.105.024024">doi:10.1103/PhysRevD.105.024024</a>. <a class="footnote-backref" href="#fnref:103" title="Jump back to footnote 103 in the text">↩</a></p> 1149</li> 1150<li id="fn:104"> 1151<p>Adam Coogan, Thomas D. P. Edwards, Horng Sheng Chia, Richard N. George, Katherine Freese, Cody Messick, Christian N. Setzer, Christoph Weniger, and Aaron Zimmerman. Efficient gravitational wave template bank generation with differentiable waveforms. <em>Physical Review D</em>, 106<span><span class="MathJax_Preview">12</span>
1151<script type="math/tex">12</script>
1151</span>:122001, December 2022. Comment: 15 pages, 6 figures. Comments welcome! Code can be found at https://github.com/adam-coogan/diffbank. <a href="https://arxiv.org/abs/2202.09380">arXiv:2202.09380</a>, <a href="https://doi.org/10.1103/PhysRevD.106.122001">doi:10.1103/PhysRevD.106.122001</a>. <a class="footnote-backref" href="#fnref:104" title="Jump back to footnote 104 in the text">↩</a></p> 1152</li> 1153<li id="fn:105"> 1154<p>Felipe F. Freitas, Carlos A. R. Herdeiro, António P. Morais, António Onofre, Roman Pasechnik, Eugen Radu, Nicolas Sanchis-Gual, and Rui Santos. Generating gravitational waveform libraries of exotic compact binaries with deep learning. <em>arXiv:2203.01267 [gr-qc]</em>, March 2022. Comment: 20 pages, 13 figures, 4 tables. <a href="https://arxiv.org/abs/2203.01267">arXiv:2203.01267</a>. <a class="footnote-backref" href="#fnref:105" title="Jump back to footnote 105 in the text">↩</a></p> 1155</li> 1156<li id="fn:106"> 1157<p>Styliani-Christina Fragkouli, Paraskevi Nousi, Nikolaos Passalis, Panagiotis Iosif, Nikolaos Stergioulas, and Anastasios Tefas. Deep residual error and bag-of-tricks learning for gravitational wave surrogate modeling. <em>Applied Soft Computing</em>, 147:110746, 2023. <a href="https://arxiv.org/abs/2203.08434">arXiv:2203.08434</a>, <a href="https://doi.org/10.1016/j.asoc.2023.110746">doi:10.1016/j.asoc.2023.110746</a>. <a class="footnote-backref" href="#fnref:106" title="Jump back to footnote 106 in the text">↩</a></p> 1158</li> 1159<li id="fn:107"> 1160<p>Lucy M. Thomas, Geraint Pratten, and Patricia Schmidt. Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks. <em>Physical Review D: Particles and Fields</em>, 106<span><span class="MathJax_Preview">arXiv:2205\.14066</span>
1160<script type="math/tex">arXiv:2205\.14066</script>
1160</span>:104029, November 2022. Comment: 18 pages <span><span class="MathJax_Preview">including appendix and bibliography</span>
1160<script type="math/tex">including appendix and bibliography</script>
1160</span>, 13 figures. <a href="https://arxiv.org/abs/2205.14066">arXiv:2205.14066</a>, <a href="https://doi.org/10.1103/PhysRevD.106.104029">doi:10.1103/PhysRevD.106.104029</a>. <a class="footnote-backref" href="#fnref:107" title="Jump back to footnote 107 in the text">↩</a></p> 1161</li> 1162<li id="fn:108"> 1163<p>Deborah Ferguson. Optimizing the placement of numerical relativity simulations using a mismatch predicting neural network. <em>Physical Review D</em>, 107<span><span class="MathJax_Preview">2</span>
1163<script type="math/tex">2</script>
1163</span>:024034, January 2023. Comment: 14 pages, 11 figures. <a href="https://arxiv.org/abs/2209.15144">arXiv:2209.15144</a>, <a href="https://doi.org/10.1103/PhysRevD.107.024034">doi:10.1103/PhysRevD.107.024034</a>. <a class="footnote-backref" href="#fnref:108" title="Jump back to footnote 108 in the text">↩</a></p> 1164</li> 1165<li id="fn:109"> 1166<p>Jacopo Tissino, Gregorio Carullo, Matteo Breschi, Rossella Gamba, Stefano Schmidt, and Sebastiano Bernuzzi. Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning. <em>Physical Review D: Particles and Fields</em>, 107<span><span class="MathJax_Preview">8</span>
1166<script type="math/tex">8</script>
1166</span>:084037, April 2023. Comment: 20 pages, 10 figures. <a href="https://arxiv.org/abs/2210.15684">arXiv:2210.15684</a>, <a href="https://doi.org/10.1103/PhysRevD.107.084037">doi:10.1103/PhysRevD.107.084037</a>. <a class="footnote-backref" href="#fnref:109" title="Jump back to footnote 109 in the text">↩</a></p> 1167</li> 1168<li id="fn:110"> 1169<p>Tibério Pereira and Riccardo Sturani. Deep learning waveform anomaly detector for numerical relativity catalogs. October 2022. Comment: 7 pages, 10 figures. <a href="https://arxiv.org/abs/2210.07299">arXiv:2210.07299</a>. <a class="footnote-backref" href="#fnref:110" title="Jump back to footnote 110 in the text">↩</a></p> 1170</li> 1171<li id="fn:111"> 1172<p>Michael L. Katz, Alvin J. K. Chua, Lorenzo Speri, Niels Warburton, and Scott A. Hughes. Fast extreme-mass-ratio-inspiral waveforms: New tools for millihertz gravitational-wave data analysis. <em>Physical Review D</em>, 104<span><span class="MathJax_Preview">6</span>
1172<script type="math/tex">6</script>
1172</span>:064047, September 2021. All the code is publicly available as open-source software. Black Hole Perturbation Toolkit, Website: \href https://bhptoolkit .org/FastEMRIWaveforms_main.htmlhttps://bhptoolkit .org/FastEMRIWaveforms_main.html. Code releases with individual Document Object Identifier <span><span class="MathJax_Preview">DOI</span>
1172<script type="math/tex">DOI</script>
1172</span> are available on on Zenodo. Comment: 26 pages, 12 Figures, FastEMRIWaveforms Package: bhptoolkit.org/FastEMRIWaveforms/. <a href="https://arxiv.org/abs/2104.04582">arXiv:2104.04582</a>, <a href="https://doi.org/10.1103/PhysRevD.104.064047">doi:10.1103/PhysRevD.104.064047</a>. <a class="footnote-backref" href="#fnref:111" title="Jump back to footnote 111 in the text">↩</a></p> 1173</li> 1174<li id="fn:112"> 1175<p>Daniel George and E. A. Huerta. Deep neural networks to enable real-time multimessenger astrophysics. <em>Physical Review D</em>, 97<span><span class="MathJax_Preview">4</span>
1175<script type="math/tex">4</script>
1175</span>:044039, February 2018. <a href="https://arxiv.org/abs/1701.00008">arXiv:1701.00008</a>, <a href="https://doi.org/10.1103/PhysRevD.97.044039">doi:10.1103/PhysRevD.97.044039</a>. <a class="footnote-backref" href="#fnref:112" title="Jump back to footnote 112 in the text">↩</a><a class="footnote-backref" href="#fnref2:112" title="Jump back to footnote 112 in the text">↩</a></p> 1176</li> 1177<li id="fn:113"> 1178<p>Daniel George and E.A. Huerta. Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data. <em>Physics Letters B</em>, 778:64â70, March 2018. <a href="https://doi.org/10.1016/j.physletb.2017.12.053">doi:10.1016/j.physletb.2017.12.053</a>. <a class="footnote-backref" href="#fnref:113" title="Jump back to footnote 113 in the text">↩</a></p> 1179</li> 1180<li id="fn:114"> 1181<p>Daniel George and E. A. Huerta. Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data. December 2017. Comment: Camera-ready <span><span class="MathJax_Preview">final</span>
1181<script type="math/tex">final</script>
1181</span> version accepted to NIPS 2017 conference workshop on Deep Learning for Physical Sciences and selected for contributed talk. Also awarded 1st place at ACM SRC at SC17. Extended article: arXiv:1711.03121. <a href="https://arxiv.org/abs/1711.07966">arXiv:1711.07966</a>. <a class="footnote-backref" href="#fnref:114" title="Jump back to footnote 114 in the text">↩</a></p> 1182</li> 1183<li id="fn:115"> 1184<p>Hongyu Shen, E A Huerta, Eamonn O’Shea, Prayush Kumar, and Zhizhen Zhao. Statistically-informed deep learning for gravitational wave parameter estimation. <em>Machine Learning: Science and Technology</em>, 3<span><span class="MathJax_Preview">1</span>
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1187<script type="math/tex">14</script>
1187</span>:141103, 2017-12-17, 2018-04. <a href="https://arxiv.org/abs/1712.06041v2">arXiv:1712.06041v2</a>, <a href="https://doi.org/10.1103/PhysRevLett.120.141103">doi:10.1103/PhysRevLett.120.141103</a>. <a class="footnote-backref" href="#fnref:116" title="Jump back to footnote 116 in the text">↩</a></p> 1188</li> 1189<li id="fn:117"> 1190<p>Xiang-Ru Li, Wo-Liang Yu, Xi-Long Fan, and G. Jogesh Babu. Some optimizations on detecting gravitational wave using convolutional neural network. <em>Frontiers of Physics</em>, 15<span><span class="MathJax_Preview">5</span>
1190<script type="math/tex">5</script>
1190</span>:54501, June 2020. <a href="https://arxiv.org/abs/1712.00356">arXiv:1712.00356</a>, <a href="https://doi.org/10.1007/s11467-020-0966-4">doi:10.1007/s11467-020-0966-4</a>. <a class="footnote-backref" href="#fnref:117" title="Jump back to footnote 117 in the text">↩</a></p> 1191</li> 1192<li id="fn:118"> 1193<p>S. J. Kapadia, T. Dent, and T. Dal Canton. Classifier for gravitational-wave inspiral signals in nonideal single-detector data. <em>Physical Review D</em>, 96<span><span class="MathJax_Preview">10</span>
1193<script type="math/tex">10</script>
1193</span>:104015, November 2017. Comment: 14 pages, 8 figures. <a href="https://arxiv.org/abs/1709.02421v1">arXiv:1709.02421v1</a>, <a href="https://doi.org/10.1103/PhysRevD.96.104015">doi:10.1103/PhysRevD.96.104015</a>. <a class="footnote-backref" href="#fnref:118" title="Jump back to footnote 118 in the text">↩</a></p> 1194</li> 1195<li id="fn:119"> 1196<p>Zhoujian Cao, Wang He, and Jianyang Zhu. Initial study on the application of deep learning to the Gravitational Wave data analysis. <em>Journal of Henan Normal University</em>, 2018. <a href="https://doi.org/10.16366/j.cnki.1000-2367.2018.02.005">doi:10.16366/j.cnki.1000-2367.2018.02.005</a>. <a class="footnote-backref" href="#fnref:119" title="Jump back to footnote 119 in the text">↩</a></p> 1197</li> 1198<li id="fn:120"> 1199<p>XiLong Fan, Jin Li, Xin Li, YuanHong Zhong, and JunWei Cao. Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors. <em>Science China Physics, Mechanics & Astronomy</em>, 62<span><span class="MathJax_Preview">6</span>
1199<script type="math/tex">6</script>
1199</span>:969512, 2019. <a href="https://doi.org/10.1007/s11433-018-9321-7">doi:10.1007/s11433-018-9321-7</a>. <a class="footnote-backref" href="#fnref:120" title="Jump back to footnote 120 in the text">↩</a><a class="footnote-backref" href="#fnref2:120" title="Jump back to footnote 120 in the text">↩</a></p> 1200</li> 1201<li id="fn:121"> 1202<p>Hua-Mei Luo, Wenbin Lin, Zu-Cheng Chen, and Qing-Guo Huang. Extraction of gravitational wave signals with optimized convolutional neural network. <em>Frontiers of Physics</em>, 15<span><span class="MathJax_Preview">1</span>
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1205<script type="math/tex">2</script>
1205</span>:24602, 2019-10-23, 2019-11. <a href="https://arxiv.org/abs/1910.10525v1">arXiv:1910.10525v1</a>, <a href="https://doi.org/10.1007/s11467-019-0935-y">doi:10.1007/s11467-019-0935-y</a>. <a class="footnote-backref" href="#fnref:122" title="Jump back to footnote 122 in the text">↩</a></p> 1206</li> 1207<li id="fn:123"> 1208<p>Li-Li Wang, Jin Li, Nan Yang, and Xin Li. Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networks. <em>New Journal of Physics</em>, 21<span><span class="MathJax_Preview">4</span>
1208<script type="math/tex">4</script>
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1373</span>:123027, June 2022. Comment: 12 pages, 4 figures \textbf Contents \begin itemize \item \href zotero://open-pdf/0_RRKIEQZU/1Abstract \item \href zotero://open-pdf/0_RRKIEQZU/1I Introduction \item \href zotero://open-pdf/0_RRKIEQZU/2II Basics of EMRI detection \begin itemize \item \href zotero://open-pdf/0_RRKIEQZU/2A Basic astronomy of EMRIs \item \href zotero://open-pdf/0_RRKIEQZU/2B Waveform models of EMRIs \item \href zotero://open-pdf/0_RRKIEQZU/3C The TianQin mission \end itemize \item \href zotero://open-pdf/0_RRKIEQZU/4III convolutional neural networks for detection \begin itemize \item \href zotero://open-pdf/0_RRKIEQZU/4A Data preparation \item \href zotero://open-pdf/0_RRKIEQZU/6B Mathematical model \item \href zotero://open-pdf/0_RRKIEQZU/6C The CNN architecture \end itemize \item \href zotero://open-pdf/0_RRKIEQZU/6IV Search procedure \begin itemize \item \href zotero://open-pdf/0_RRKIEQZU/6A Training phase \item \href zotero://open-pdf/0_RRKIEQZU/7B Testing phase \end itemize \item \href zotero://open-pdf/0_RRKIEQZU/7V Results \begin itemize \item \href zotero://open-pdf/0_RRKIEQZU/7A Validity \item \href zotero://open-pdf/0_RRKIEQZU/8B Sensitivity \end itemize \item \href zotero://open-pdf/0_RRKIEQZU/10VI conclusions and Future works \item \href zotero://open-pdf/0_RRKIEQZU/10VII Acknowledgments \item \href zotero://open-pdf/0_RRKIEQZU/10 References \end itemize. <a href="https://arxiv.org/abs/2202.07158">arXiv:2202.07158</a>, <a href="https://doi.org/10.1103/PhysRevD.105.123027">doi:10.1103/PhysRevD.105.123027</a>. <a class="footnote-backref" href="#fnref:178" title="Jump back to footnote 178 in the text">↩</a></p> 1374</li> 1375<li id="fn:179"> 1376<p>Mohammadtaher Safarzadeh, Asad Khan, E. A. Huerta, and Martin Wattenberg. Interpreting a Machine Learning Model for Detecting Gravitational Waves. <em>arXiv:2202.07399 [astro-ph, physics:gr-qc]</em>, February 2022. Comment: 19 pages, to be submitted, comments are welcome. Movies based on this work can be accessed via: https://www.youtube.com/watch?v=SXFGMOtJwn0 https://www.youtube.com/watch?v=itVCj9gpmAs. <a href="https://arxiv.org/abs/2202.07399">arXiv:2202.07399</a>. <a class="footnote-backref" href="#fnref:179" title="Jump back to footnote 179 in the text">↩</a></p> 1377</li> 1378<li id="fn:180"> 1379<p>Sunil Choudhary, Anupreeta More, Sudhagar Suyamprakasam, and Sukanta Bose. Deep learning network to distinguish binary black hole signals from short-duration noise transients. <em>Physical Review D</em>, 107<span><span class="MathJax_Preview">2</span>
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1379</span>:024030, January 2023. Comment: 11 pages, 8 figures and 2 tables. Reviewed by LIGO Scientific Collaboration <span><span class="MathJax_Preview">LSC</span>
1379<script type="math/tex">LSC</script>
1379</span> with preprint number LIGO-P2100485. <a href="https://arxiv.org/abs/2202.08671">arXiv:2202.08671</a>, <a href="https://doi.org/10.1103/PhysRevD.107.024030">doi:10.1103/PhysRevD.107.024030</a>. <a class="footnote-backref" href="#fnref:180" title="Jump back to footnote 180 in the text">↩</a></p> 1380</li> 1381<li id="fn:181"> 1382<p>Connor McIsaac and Ian Harry. Using machine learning to auto-tune chi-squared tests for gravitational wave searches. <em>arXiv:2203.03449 [astro-ph, physics:gr-qc]</em>, March 2022. Comment: 10 pages, 5 figures. Supplementary data: https://icg-gravwaves.github.io/chisqnet/. <a href="https://arxiv.org/abs/2203.03449">arXiv:2203.03449</a>. <a class="footnote-backref" href="#fnref:181" title="Jump back to footnote 181 in the text">↩</a></p> 1383</li> 1384<li id="fn:182"> 1385<p>Meng-Qin Jiang, Nan Yang, and Jin Li. Identify real gravitational wave events in the LIGO-Virgo catalog GWTC-1 and GWTC-2 with convolutional neural network. <em>Frontiers of Physics</em>, 17<span><span class="MathJax_Preview">5</span>
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1385</span>:54501, March 2022. <a href="https://doi.org/10.1007/s11467-021-1150-1">doi:10.1007/s11467-021-1150-1</a>. <a class="footnote-backref" href="#fnref:182" title="Jump back to footnote 182 in the text">↩</a></p> 1386</li> 1387<li id="fn:183"> 1388<p>Grégory Baltus, Justin Janquart, Melissa Lopez, Harsh Narola, and Jean-René Cudell. Convolutional neural network for gravitational-wave early alert: Going down in frequency. <em>Physical Review D</em>, 106<span><span class="MathJax_Preview">arXiv:2205\.04750</span>
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1388</span>:042002, August 2022. Comment: 11 pages, 10 figures. <a href="https://arxiv.org/abs/2205.04750">arXiv:2205.04750</a>, <a href="https://doi.org/10.1103/PhysRevD.106.042002">doi:10.1103/PhysRevD.106.042002</a>. <a class="footnote-backref" href="#fnref:183" title="Jump back to footnote 183 in the text">↩</a></p> 1389</li> 1390<li id="fn:184"> 1391<p>Kyungmin Kim, Joongoo Lee, Otto A. Hannuksela, and Tjonnie G. F. Li. Deep Learningâbased search for microlensing signature from binary black hole events in GWTC-1 and -2. <em>The Astrophysical Journal</em>, 938<span><span class="MathJax_Preview">arXiv:2206\.08234</span>
1391<script type="math/tex">arXiv:2206\.08234</script>
1391</span>:157, October 2022. Comment: 11 pages, 6 figures, 4 tables. <a href="https://arxiv.org/abs/2206.08234">arXiv:2206.08234</a>, <a href="https://doi.org/10.3847/1538-4357/ac92f3">doi:10.3847/1538-4357/ac92f3</a>. <a class="footnote-backref" href="#fnref:184" title="Jump back to footnote 184 in the text">↩</a></p> 1392</li> 1393<li id="fn:185"> 1394<p>Chetan Verma, Amit Reza, Gurudatt Gaur, Dilip Krishnaswamy, and Sarah Caudill. Can Convolution Neural Networks Be Used for Detection of Gravitational Waves from Precessing Black Hole Systems? June 2022. <a href="https://arxiv.org/abs/2206.12673">arXiv:2206.12673</a>. <a class="footnote-backref" href="#fnref:185" title="Jump back to footnote 185 in the text">↩</a></p> 1395</li> 1396<li id="fn:186"> 1397<p>João Aveiro, Felipe F. Freitas, Márcio Ferreira, Antonio Onofre, Constançça Providência, Gonççalo Gonççalves, and José A. Font. Identification of binary neutron star mergers in gravitational-wave data using object-detection machine learning models. <em>Physical Review D</em>, 106<span><span class="MathJax_Preview">8</span>
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1397</span>:084059, October 2022. Comment: 11 pages, 9 figures. <a href="https://arxiv.org/abs/2207.00591">arXiv:2207.00591</a>, <a href="https://doi.org/10.1103/PhysRevD.106.084059">doi:10.1103/PhysRevD.106.084059</a>. <a class="footnote-backref" href="#fnref:186" title="Jump back to footnote 186 in the text">↩</a></p> 1398</li> 1399<li id="fn:187"> 1400<p>Michael Andrews, Manfred Paulini, Luke Sellers, Alexey Bobrick, Gianni Martire, and Haydn Vestal. DeepSNR: A deep learning foundation for offline gravitational wave detection. July 2022. Comment: 16 pages, 6 figures. <a href="https://arxiv.org/abs/2207.04749">arXiv:2207.04749</a>. <a class="footnote-backref" href="#fnref:187" title="Jump back to footnote 187 in the text">↩</a></p> 1401</li> 1402<li id="fn:188"> 1403<p>Tianyu Zhao, Ruoxi Lyu, He Wang, Zhoujian Cao, and Zhixiang Ren. Space-based gravitational wave signal detection and extraction with deep neural network. <em>Communications Physics</em>, 6<span><span class="MathJax_Preview">1</span>
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1415</span>:045054, December 2023. <a href="https://arxiv.org/abs/2206.06004">arXiv:2206.06004</a>, <a href="https://doi.org/10.1088/2632-2153/ad1200">doi:10.1088/2632-2153/ad1200</a>. <a class="footnote-backref" href="#fnref:192" title="Jump back to footnote 192 in the text">↩</a></p> 1416</li> 1417<li id="fn:193"> 1418<p>Marlin B. Schäfer, OnÄrÅej Zelenka, Alexander H. Nitz, He Wang, Shichao Wu, Zong-Kuan Guo, Zhoujian Cao, Zhixiang Ren, Paraskevi Nousi, Nikolaos Stergioulas, Panagiotis Iosif, Alexandra E. Koloniari, Anastasios Tefas, Nikolaos Passalis, Francesco Salemi, Gabriele Vedovato, Sergey Klimenko, Tanmaya Mishra, Bernd Brügmann, Elena Cuoco, E. A. Huerta, Chris Messenger, and Frank Ohme. First machine learning gravitational-wave search mock data challenge. <em>Physical Review D</em>, 107<span><span class="MathJax_Preview">2</span>
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1418</span>:023021, January 2023. Comment: 25 pages, 6 figures, 4 tables, additional material available at https://github.com/gwastro/ml-mock-data-challenge-1. <a href="https://arxiv.org/abs/2209.11146">arXiv:2209.11146</a>, <a href="https://doi.org/10.1103/PhysRevD.107.023021">doi:10.1103/PhysRevD.107.023021</a>. <a class="footnote-backref" href="#fnref:193" title="Jump back to footnote 193 in the text">↩</a></p> 1419</li> 1420<li id="fn:194"> 1421<p>Grégory Baltus. <em>A Machine Learning Approach to the Search for Gravitational Waves Emitted by Light Systems</em>. PhD thesis, ULiège - Université de Liège [Sciences], September 2022. <a class="footnote-backref" href="#fnref:194" title="Jump back to footnote 194 in the text">↩</a></p> 1422</li> 1423<li id="fn:195"> 1424<p>Hao Zhang, Zhijun Zhu, Minglei Fu, Minchao Hu, Kezhen Rong, Dmytro Lande, Dmytro Manko, and Zaher Mundher Yaseen. Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network. <em>Computational Intelligence and Neuroscience</em>, 2022:5892188, September 2022. <a href="https://doi.org/10.1155/2022/5892188">doi:10.1155/2022/5892188</a>. <a class="footnote-backref" href="#fnref:195" title="Jump back to footnote 195 in the text">↩</a></p> 1425</li> 1426<li id="fn:196"> 1427<p>Charles Badger, Katarina Martinovic, Alejandro Torres-Forné, Mairi Sakellariadou, and José A. Font. Dictionary learning: A novel approach to detecting binary black holes in the presence of galactic noise with LISA. <em>Physical Review Letters</em>, 130<span><span class="MathJax_Preview">9</span>
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2006</span>:044051, August 2022. Comment: 26 pages, 19 figures. <a href="https://arxiv.org/abs/2206.03006">arXiv:2206.03006</a>, <a href="https://doi.org/10.1103/PhysRevD.106.044051">doi:10.1103/PhysRevD.106.044051</a>. <a class="footnote-backref" href="#fnref:389" title="Jump back to footnote 389 in the text">↩</a></p> 2007</li> 2008<li id="fn:390"> 2009<p>Raimon Luna, Juan Calderón Bustillo, Juan José Seoane MartÃnez, Alejandro Torres-Forné, and José A. Font. Solving the Teukolsky equation with physics-informed neural networks. December 2022. Comment: 12 pages, 7 figures. <a href="https://arxiv.org/abs/2212.06103">arXiv:2212.06103</a>. <a class="footnote-backref" href="#fnref:390" title="Jump back to footnote 390 in the text">↩</a></p> 2010</li> 2011<li id="fn:391"> 2012<p>Ehsan Hatefi, Armin Hatefi, and Roberto J. López-Sastre. Analysis of Black Hole Solutions in Parabolic Class Using Neural Networks. February 2023. Comment: 20 pages, 13 figures. <a href="https://arxiv.org/abs/2302.04619">arXiv:2302.04619</a>. <a class="footnote-backref" href="#fnref:391" title="Jump back to footnote 391 in the text">↩</a></p> 2013</li> 2014<li id="fn:392"> 2015<p>Federico Sabbatini and Catia Grimani. Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA. February 2023. Comment: Submitted to Environmental Modelling & Software. <a href="https://arxiv.org/abs/2302.06740">arXiv:2302.06740</a>. <a class="footnote-backref" href="#fnref:392" title="Jump back to footnote 392 in the text">↩</a></p> 2016</li> 2017<li id="fn:393"> 2018<p>Peter Xiangyuan Ma and Gabriele Vajente. A deep learning technique to control the non-linear dynamics of a gravitational-wave interferometer. <em>Classical and Quantum Gravity</em>, 41<span><span class="MathJax_Preview">4</span>
2018<script type="math/tex">4</script>
2018</span>:045003, January 2024. <a href="https://arxiv.org/abs/2302.07921">arXiv:2302.07921</a>, <a href="https://doi.org/10.1088/1361-6382/ad1daa">doi:10.1088/1361-6382/ad1daa</a>. <a class="footnote-backref" href="#fnref:393" title="Jump back to footnote 393 in the text">↩</a></p> 2019</li> 2020<li id="fn:394"> 2021<p>Shawn G. Rosofsky and E. A. Huerta. Magnetohydrodynamics with Physics Informed Neural Operators. February 2023. Comment: 13 pages, 9 figures, 1 table. First application of physics informed neural operators to solve magnetohydrodynamics equations. <a href="https://arxiv.org/abs/2302.08332">arXiv:2302.08332</a>. <a class="footnote-backref" href="#fnref:394" title="Jump back to footnote 394 in the text">↩</a></p> 2022</li> 2023<li id="fn:395"> 2024<p>Sung Hak Lim, Eric Putney, Matthew R. Buckley, and David Shih. Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3. May 2023. Comment: 19 pages, 13 figures, 3 tables. <a href="https://arxiv.org/abs/2305.13358">arXiv:2305.13358</a>. <a class="footnote-backref" href="#fnref:395" title="Jump back to footnote 395 in the text">↩</a></p> 2025</li> 2026<li id="fn:396"> 2027<p>Konstantinos F. Dialektopoulos, Purba Mukherjee, Jackson Levi Said, and Jurgen Mifsud. Neural network reconstruction of scalar-tensor cosmology. May 2023. <a href="https://arxiv.org/abs/2305.15500">arXiv:2305.15500</a>. <a class="footnote-backref" href="#fnref:396" title="Jump back to footnote 396 in the text">↩</a></p> 2028</li> 2029<li id="fn:397"> 2030<p>Ziming Liu and Max Tegmark. Machine-learning hidden symmetries. <em>Physical Review Letters</em>, 128<span><span class="MathJax_Preview">18</span>
2030<script type="math/tex">18</script>
2030</span>:180201, May 2022. Comment: Replaced to match accepted PRL version. Improved training, discussion & noise modeling. 14 pages & 4 figs including supplementary material. <a href="https://arxiv.org/abs/2109.09721">arXiv:2109.09721</a>, <a href="https://doi.org/10.1103/PhysRevLett.128.180201">doi:10.1103/PhysRevLett.128.180201</a>. <a class="footnote-backref" href="#fnref:397" title="Jump back to footnote 397 in the text">↩</a></p> 2031</li> 2032</ol> 2033</div></div> 2034 </div> 2035 </div> 2036 2037 <footer class="col-md-12"> 2038 <hr> 2039 <p>This site (Survey4GWML) is licensed under the <a href='https://github.com/iphysresearch/Survey4GWML/blob/master/LICENSE.md'>MIT license</a></p> 2040 <p>Documentation built with <a href="https://www.mkdocs.org/">MkDocs</a>.</p> 2041 </footer> 2042
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