{
  "id": 249993,
  "title": "Papers on Gravitational Wave Detection and Machine Learning",
  "url": "/competitions/g2net-gravitational-wave-detection/discussion/249993",
  "author_name": "Charlie Craine",
  "post_date": "2021-06-30T17:52:39.750000",
  "votes": 108,
  "comment_count": 27,
  "views": 0,
  "content": "<p>Hey everyone!</p>\n<p>I wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!</p>\n<p><strong>Research Papers:</strong></p>\n<ul>\n<li><p><a href=\"https://arxiv.org/abs/2106.14089\" target=\"_blank\">Accelerating Recurrent Neural Networks for Gravitational Wave Experiments</a> - This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such as black hole mergers which happen at unknown times and of varying durations, producing time-series data. We have developed a new architecture capable of accelerating RNN inference for analyzing time-series data from LIGO detectors. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.13785\" target=\"_blank\">Inference with finite time series: Observing the gravitational Universe through windows</a> - Time series analysis is ubiquitous in many fields of science including gravitational-wave astronomy, where strain time series are analyzed to infer the nature of gravitational-wave sources, e.g., black holes and neutron stars. It is common in gravitational-wave transient studies to apply a tapered window function to reduce the effects of spectral artifacts from the sharp edges of data segments. We show that the conventional analysis of tapered data fails to take into account covariance between frequency bins, which arises for all finite time series -- no matter the choice of window function. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.12466\" target=\"_blank\">Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning</a> - A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pairs. As a proof of principle, we simulate non-spinning binary black hole events added to Gaussian noise, and train the machine on their time-frequency maps (Q-transforms) and localisation skymaps (using Bayestar), both of which can be generated in seconds. We show that the trained machine is able to accurately identify lensed pairs with efficiencies comparable to existing Bayesian methods.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.12594\" target=\"_blank\">Real-time gravitational-wave science with neural posterior estimation</a> - We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog and find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.13769\" target=\"_blank\">The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors</a> - The next generation of gravitational-wave experiments, such as Einstein Telescope, Cosmic Explorer and LISA, will test the primordial black hole scenario. We provide a forecast for the minimum testable value of the abundance of primordial black holes as a function of their masses for both the unclustered and clustered spatial distributions at formation. In particular, we show that these instruments may test abundances, relative to the dark matter, as low as 10−10.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.14194\" target=\"_blank\">Memory Effects in Scattering from Accelerating Bodies</a> - Here we investigate theoretically and experimentally an interaction regime, which is neither relativistic nor adiabatic. The test model considers an accelerating scatterer with a long-lasting relaxation memory. The slow decay rates violate the instantaneous reaction assumption of quasi-stationarity, introducing non-Markovian contributions to the scattering process. Memory signatures in scattering from a rotating dipole are studied theoretically, showing symmetry breaking of micro-Doppler combs. A quasi-stationary numeric analysis of scattering in the short memory limit is proposed and validated experimentally with an example of electromagnetic pulses interacting with a rotating wire.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.15163\" target=\"_blank\">Observation of gravitational waves from two neutron star-black hole coalescences</a> - We report the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run with properties consistent with neutron star-black hole (NSBH) binaries.</p></li>\n</ul>",
  "messages": [
    {
      "id": 1371150,
      "postDate": "2021-06-30T17:52:39.750Z",
      "content": "<p>Hey everyone!</p>\n<p>I wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!</p>\n<p><strong>Research Papers:</strong></p>\n<ul>\n<li><p><a href=\"https://arxiv.org/abs/2106.14089\" target=\"_blank\">Accelerating Recurrent Neural Networks for Gravitational Wave Experiments</a> - This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such as black hole mergers which happen at unknown times and of varying durations, producing time-series data. We have developed a new architecture capable of accelerating RNN inference for analyzing time-series data from LIGO detectors. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.13785\" target=\"_blank\">Inference with finite time series: Observing the gravitational Universe through windows</a> - Time series analysis is ubiquitous in many fields of science including gravitational-wave astronomy, where strain time series are analyzed to infer the nature of gravitational-wave sources, e.g., black holes and neutron stars. It is common in gravitational-wave transient studies to apply a tapered window function to reduce the effects of spectral artifacts from the sharp edges of data segments. We show that the conventional analysis of tapered data fails to take into account covariance between frequency bins, which arises for all finite time series -- no matter the choice of window function. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.12466\" target=\"_blank\">Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning</a> - A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pairs. As a proof of principle, we simulate non-spinning binary black hole events added to Gaussian noise, and train the machine on their time-frequency maps (Q-transforms) and localisation skymaps (using Bayestar), both of which can be generated in seconds. We show that the trained machine is able to accurately identify lensed pairs with efficiencies comparable to existing Bayesian methods.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.12594\" target=\"_blank\">Real-time gravitational-wave science with neural posterior estimation</a> - We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog and find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.13769\" target=\"_blank\">The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors</a> - The next generation of gravitational-wave experiments, such as Einstein Telescope, Cosmic Explorer and LISA, will test the primordial black hole scenario. We provide a forecast for the minimum testable value of the abundance of primordial black holes as a function of their masses for both the unclustered and clustered spatial distributions at formation. In particular, we show that these instruments may test abundances, relative to the dark matter, as low as 10−10.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.14194\" target=\"_blank\">Memory Effects in Scattering from Accelerating Bodies</a> - Here we investigate theoretically and experimentally an interaction regime, which is neither relativistic nor adiabatic. The test model considers an accelerating scatterer with a long-lasting relaxation memory. The slow decay rates violate the instantaneous reaction assumption of quasi-stationarity, introducing non-Markovian contributions to the scattering process. Memory signatures in scattering from a rotating dipole are studied theoretically, showing symmetry breaking of micro-Doppler combs. A quasi-stationary numeric analysis of scattering in the short memory limit is proposed and validated experimentally with an example of electromagnetic pulses interacting with a rotating wire.</p></li>\n<li><p><a href=\"https://arxiv.org/abs/2106.15163\" target=\"_blank\">Observation of gravitational waves from two neutron star-black hole coalescences</a> - We report the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run with properties consistent with neutron star-black hole (NSBH) binaries.</p></li>\n</ul>",
      "rawMarkdown": "Hey everyone!\n\nI wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\n**Research Papers:**\n\n- [Accelerating Recurrent Neural Networks for Gravitational Wave Experiments](https://arxiv.org/abs/2106.14089) - This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such as black hole mergers which happen at unknown times and of varying durations, producing time-series data. We have developed a new architecture capable of accelerating RNN inference for analyzing time-series data from LIGO detectors. \n\n- [Inference with finite time series: Observing the gravitational Universe through windows](https://arxiv.org/abs/2106.13785) - Time series analysis is ubiquitous in many fields of science including gravitational-wave astronomy, where strain time series are analyzed to infer the nature of gravitational-wave sources, e.g., black holes and neutron stars. It is common in gravitational-wave transient studies to apply a tapered window function to reduce the effects of spectral artifacts from the sharp edges of data segments. We show that the conventional analysis of tapered data fails to take into account covariance between frequency bins, which arises for all finite time series -- no matter the choice of window function. \n\n- [Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning](https://arxiv.org/abs/2106.12466) - A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pairs. As a proof of principle, we simulate non-spinning binary black hole events added to Gaussian noise, and train the machine on their time-frequency maps (Q-transforms) and localisation skymaps (using Bayestar), both of which can be generated in seconds. We show that the trained machine is able to accurately identify lensed pairs with efficiencies comparable to existing Bayesian methods.\n\n- [Real-time gravitational-wave science with neural posterior estimation](https://arxiv.org/abs/2106.12594) - We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog and find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.\n\n- [The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors](https://arxiv.org/abs/2106.13769) - The next generation of gravitational-wave experiments, such as Einstein Telescope, Cosmic Explorer and LISA, will test the primordial black hole scenario. We provide a forecast for the minimum testable value of the abundance of primordial black holes as a function of their masses for both the unclustered and clustered spatial distributions at formation. In particular, we show that these instruments may test abundances, relative to the dark matter, as low as 10−10.\n\n- [Memory Effects in Scattering from Accelerating Bodies](https://arxiv.org/abs/2106.14194) - Here we investigate theoretically and experimentally an interaction regime, which is neither relativistic nor adiabatic. The test model considers an accelerating scatterer with a long-lasting relaxation memory. The slow decay rates violate the instantaneous reaction assumption of quasi-stationarity, introducing non-Markovian contributions to the scattering process. Memory signatures in scattering from a rotating dipole are studied theoretically, showing symmetry breaking of micro-Doppler combs. A quasi-stationary numeric analysis of scattering in the short memory limit is proposed and validated experimentally with an example of electromagnetic pulses interacting with a rotating wire.\n\n- [Observation of gravitational waves from two neutron star-black hole coalescences](https://arxiv.org/abs/2106.15163) - We report the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run with properties consistent with neutron star-black hole (NSBH) binaries.",
      "votes": 107
    },
    {
      "id": 1375086,
      "postDate": "2021-07-03T20:33:54.377Z",
      "content": "<p>This one might be relevant:</p>\n<p><a href=\"https://arxiv.org/abs/1908.11170\" target=\"_blank\"><strong>A guide to LIGO-Virgo detector noise and\nextraction of transient gravitational-wave signals</strong></a></p>\n<p>\"…In this paper, we provide an overview of the detector noise properties<br>\nand the data analysis techniques used to detect gravitational-wave signals and<br>\ninfer the source properties. We describe some of the checks that are performed<br>\nto validate the analyses and results from the observations of gravitational-wave<br>\nevents. We also address concerns that have been raised about various properties<br>\nof LIGO-Virgo detector noise and the correctness of our analyses as applied to the<br>\nresulting data.\"</p>",
      "rawMarkdown": "This one might be relevant:\n\n[**A guide to LIGO-Virgo detector noise and\nextraction of transient gravitational-wave signals**](https://arxiv.org/abs/1908.11170)\n\n\"...In this paper, we provide an overview of the detector noise properties\nand the data analysis techniques used to detect gravitational-wave signals and\ninfer the source properties. We describe some of the checks that are performed\nto validate the analyses and results from the observations of gravitational-wave\nevents. We also address concerns that have been raised about various properties\nof LIGO-Virgo detector noise and the correctness of our analyses as applied to the\nresulting data.\"",
      "votes": 3
    },
    {
      "id": 1371409,
      "postDate": "2021-07-01T01:23:02.547Z",
      "content": "<p>I found another:<br>\n<a href=\"https://iopscience.iop.org/article/10.1088/2632-2153/abb93a\" target=\"_blank\">Enhancing gravitational-wave science with machine learning</a> - Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.</p>",
      "rawMarkdown": "I found another:\n[Enhancing gravitational-wave science with machine learning](https://iopscience.iop.org/article/10.1088/2632-2153/abb93a) - Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.",
      "votes": 4
    },
    {
      "id": 1372017,
      "postDate": "2021-07-01T11:07:54.213Z",
      "content": "<p>Is it better for me to add to the main list or add in comments? I found four more recent papers!</p>",
      "rawMarkdown": "Is it better for me to add to the main list or add in comments? I found four more recent papers!",
      "votes": 2,
      "replies": [
        {
          "id": 1372179,
          "postDate": "2021-07-01T13:38:08.550Z",
          "content": "<p>Very insightful. Thanks for this.<br>\nPlease add to the main list. That will be much better I guess.</p>",
          "rawMarkdown": "Very insightful. Thanks for this.\nPlease add to the main list. That will be much better I guess.",
          "votes": 1
        },
        {
          "id": 1375068,
          "postDate": "2021-07-03T20:18:58.857Z",
          "content": "<p>Agree with Pratibha, the papers may get lost in a sea of comments. Much easier to navigate on the main post.</p>",
          "rawMarkdown": "Agree with Pratibha, the papers may get lost in a sea of comments. Much easier to navigate on the main post."
        },
        {
          "id": 1375084,
          "postDate": "2021-07-03T20:32:21.740Z",
          "content": "<p>Thanks <a href=\"https://www.kaggle.com/pratibha9\" target=\"_blank\">@pratibha9</a> and <a href=\"https://www.kaggle.com/kingcrab\" target=\"_blank\">@kingcrab</a> I'll add the rest to the main ones. </p>",
          "rawMarkdown": "Thanks @pratibha9 and @kingcrab I'll add the rest to the main ones. "
        }
      ]
    },
    {
      "id": 1561215,
      "postDate": "2021-10-27T12:20:40.223Z",
      "content": "<p>Hey All,</p>\n<p>Thank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey <a href=\"https://forms.gle/QP9L16niPexozyhu5\" target=\"_blank\">https://forms.gle/QP9L16niPexozyhu5</a>.</p>\n<p>Thank you all,</p>\n<p>Regards,<br>\nChris</p>",
      "rawMarkdown": "Hey All,\n\nThank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey https://forms.gle/QP9L16niPexozyhu5.\n\nThank you all,\n\nRegards,\nChris"
    },
    {
      "id": 1467585,
      "postDate": "2021-08-12T04:12:28.020Z",
      "content": "<p>Hi:<br>\nI am confusing about one question in <a href=\"url\" target=\"_blank\">https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102</a>:<br>\nIn real world, the actual gravitational wave （GW150914） arrived first at L1 and 6.9 millisecond later at H1 (to account for the detectors’ relative orientations), even though Gravitational Waves travel at the speed of light.<br>\nAnd I am puzzled that is simulated gravitational wave signal arrive at different time too (shifted before plus to different noise)? Or it was plus to noisy directly without shifted?<br>\nWelcome to discuss!!!</p>",
      "rawMarkdown": "Hi:\nI am confusing about one question in [https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102](url):\nIn real world, the actual gravitational wave （GW150914） arrived first at L1 and 6.9 millisecond later at H1 (to account for the detectors’ relative orientations), even though Gravitational Waves travel at the speed of light.\nAnd I am puzzled that is simulated gravitational wave signal arrive at different time too (shifted before plus to different noise)? Or it was plus to noisy directly without shifted?\nWelcome to discuss!!!",
      "replies": [
        {
          "id": 1479165,
          "postDate": "2021-08-18T10:17:27.143Z",
          "content": "<p>Host said waves are shifted in this simulation depending on the simulated position of wave origin.</p>",
          "rawMarkdown": "Host said waves are shifted in this simulation depending on the simulated position of wave origin.",
          "votes": 3
        }
      ]
    },
    {
      "id": 1384120,
      "postDate": "2021-07-11T13:23:51.540Z",
      "content": "<p>Very interesting!</p>",
      "rawMarkdown": "Very interesting!"
    },
    {
      "id": 1382456,
      "postDate": "2021-07-09T21:55:28.397Z",
      "content": "<p>Thank you so much for the very useful information, indeed.</p>",
      "rawMarkdown": "Thank you so much for the very useful information, indeed."
    },
    {
      "id": 1377913,
      "postDate": "2021-07-06T08:01:53.237Z",
      "content": "<p>Amazing work, and its really helpful when I struggle. Appreciate your work! <a href=\"https://www.kaggle.com/crained\" target=\"_blank\">@crained</a> </p>",
      "rawMarkdown": "Amazing work, and its really helpful when I struggle. Appreciate your work! @crained ",
      "replies": [
        {
          "id": 1378231,
          "postDate": "2021-07-06T11:45:43.713Z",
          "content": "<p>Thank you <a href=\"https://www.kaggle.com/praneshrajendrakumar\" target=\"_blank\">@praneshrajendrakumar</a> hope they help!</p>",
          "rawMarkdown": "Thank you @praneshrajendrakumar hope they help!"
        }
      ]
    },
    {
      "id": 1377901,
      "postDate": "2021-07-06T07:50:14.913Z",
      "content": "<p>Thanks for the post, saved a lot of time.</p>",
      "rawMarkdown": "Thanks for the post, saved a lot of time."
    },
    {
      "id": 1374213,
      "postDate": "2021-07-03T05:57:11.523Z",
      "content": "<p>This was very insightful. Thank you! <a href=\"https://www.kaggle.com/crained\" target=\"_blank\">@crained</a> </p>",
      "rawMarkdown": "This was very insightful. Thank you! @crained ",
      "replies": [
        {
          "id": 1374499,
          "postDate": "2021-07-03T11:16:25.723Z",
          "content": "<p>Thanks <a href=\"https://www.kaggle.com/adhithia\" target=\"_blank\">@adhithia</a> good luck with the competition!</p>",
          "rawMarkdown": "Thanks @adhithia good luck with the competition!"
        }
      ]
    },
    {
      "id": 1372408,
      "postDate": "2021-07-01T17:02:06.240Z",
      "content": "<p>Very useful information. I will try to add any papers if I find it.</p>",
      "rawMarkdown": "Very useful information. I will try to add any papers if I find it.\n"
    },
    {
      "id": 1371305,
      "postDate": "2021-06-30T21:20:47.313Z",
      "content": "<p>Thank you. This will be of great help.</p>",
      "rawMarkdown": "Thank you. This will be of great help.",
      "replies": [
        {
          "id": 1371405,
          "postDate": "2021-07-01T01:20:40.893Z",
          "content": "<p><a href=\"https://www.kaggle.com/woosungyoon\" target=\"_blank\">@woosungyoon</a> I hope it helps you in the competition!</p>",
          "rawMarkdown": "@woosungyoon I hope it helps you in the competition!"
        }
      ]
    },
    {
      "id": 1371272,
      "postDate": "2021-06-30T20:11:38.767Z",
      "content": "<p>Very helpful, thanks!</p>",
      "rawMarkdown": "Very helpful, thanks!",
      "replies": [
        {
          "id": 1371406,
          "postDate": "2021-07-01T01:21:04.397Z",
          "content": "<p>No problem <a href=\"https://www.kaggle.com/varpit94\" target=\"_blank\">@varpit94</a> … good luck on the competition. </p>",
          "rawMarkdown": "No problem @varpit94 ... good luck on the competition. "
        }
      ]
    },
    {
      "id": 1398386,
      "postDate": "2021-07-24T05:49:30.667Z",
      "rawMarkdown": "",
      "isDeleted": true
    },
    {
      "id": 1371494,
      "postDate": "2021-07-01T03:58:58.333Z",
      "content": "<p>Very helpful, Thanks for sharing🙌</p>",
      "rawMarkdown": "Very helpful, Thanks for sharing🙌",
      "votes": 1
    },
    {
      "id": 1406073,
      "postDate": "2021-07-31T13:20:06.407Z",
      "content": "<p>Thanks a lot!</p>",
      "rawMarkdown": "Thanks a lot!"
    },
    {
      "id": 1395170,
      "postDate": "2021-07-21T01:50:34.243Z",
      "content": "<p>Thanks you for sharing </p>",
      "rawMarkdown": "Thanks you for sharing "
    },
    {
      "id": 1383083,
      "postDate": "2021-07-10T13:50:06.540Z",
      "content": "<p>Thank you.</p>",
      "rawMarkdown": "Thank you."
    },
    {
      "id": 1377358,
      "postDate": "2021-07-05T19:06:41.910Z",
      "content": "<p>Thank you so much for compiling it. 👋</p>",
      "rawMarkdown": "Thank you so much for compiling it. 👋"
    }
  ],
  "comments": [
    {
      "id": 1375086,
      "author_name": "Greg Feldmann",
      "author_url": "",
      "post_date": "2021-07-03T20:33:54.377000",
      "content": "<p>This one might be relevant:</p>\n<p><a href=\"https://arxiv.org/abs/1908.11170\" target=\"_blank\"><strong>A guide to LIGO-Virgo detector noise and\nextraction of transient gravitational-wave signals</strong></a></p>\n<p>\"…In this paper, we provide an overview of the detector noise properties<br>\nand the data analysis techniques used to detect gravitational-wave signals and<br>\ninfer the source properties. We describe some of the checks that are performed<br>\nto validate the analyses and results from the observations of gravitational-wave<br>\nevents. We also address concerns that have been raised about various properties<br>\nof LIGO-Virgo detector noise and the correctness of our analyses as applied to the<br>\nresulting data.\"</p>",
      "votes": 3,
      "replies": []
    },
    {
      "id": 1371409,
      "author_name": "Charlie Craine",
      "author_url": "",
      "post_date": "2021-07-01T01:23:02.547000",
      "content": "<p>I found another:<br>\n<a href=\"https://iopscience.iop.org/article/10.1088/2632-2153/abb93a\" target=\"_blank\">Enhancing gravitational-wave science with machine learning</a> - Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.</p>",
      "votes": 4,
      "replies": []
    },
    {
      "id": 1372017,
      "author_name": "Charlie Craine",
      "author_url": "",
      "post_date": "2021-07-01T11:07:54.213000",
      "content": "<p>Is it better for me to add to the main list or add in comments? I found four more recent papers!</p>",
      "votes": 2,
      "replies": [
        {
          "id": 1372179,
          "author_name": "Pratibha",
          "author_url": "",
          "post_date": "2021-07-01T13:38:08.550000",
          "content": "<p>Very insightful. Thanks for this.<br>\nPlease add to the main list. That will be much better I guess.</p>",
          "votes": 1,
          "replies": []
        },
        {
          "id": 1375068,
          "author_name": "Greg Feldmann",
          "author_url": "",
          "post_date": "2021-07-03T20:18:58.857000",
          "content": "<p>Agree with Pratibha, the papers may get lost in a sea of comments. Much easier to navigate on the main post.</p>",
          "votes": 0,
          "replies": []
        },
        {
          "id": 1375084,
          "author_name": "Charlie Craine",
          "author_url": "",
          "post_date": "2021-07-03T20:32:21.740000",
          "content": "<p>Thanks <a href=\"https://www.kaggle.com/pratibha9\" target=\"_blank\">@pratibha9</a> and <a href=\"https://www.kaggle.com/kingcrab\" target=\"_blank\">@kingcrab</a> I'll add the rest to the main ones. </p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 1561215,
      "author_name": "ChristopherZerafa",
      "author_url": "",
      "post_date": "2021-10-27T12:20:40.223000",
      "content": "<p>Hey All,</p>\n<p>Thank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey <a href=\"https://forms.gle/QP9L16niPexozyhu5\" target=\"_blank\">https://forms.gle/QP9L16niPexozyhu5</a>.</p>\n<p>Thank you all,</p>\n<p>Regards,<br>\nChris</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1467585,
      "author_name": "SeaSideSeer",
      "author_url": "",
      "post_date": "2021-08-12T04:12:28.020000",
      "content": "<p>Hi:<br>\nI am confusing about one question in <a href=\"url\" target=\"_blank\">https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102</a>:<br>\nIn real world, the actual gravitational wave （GW150914） arrived first at L1 and 6.9 millisecond later at H1 (to account for the detectors’ relative orientations), even though Gravitational Waves travel at the speed of light.<br>\nAnd I am puzzled that is simulated gravitational wave signal arrive at different time too (shifted before plus to different noise)? Or it was plus to noisy directly without shifted?<br>\nWelcome to discuss!!!</p>",
      "votes": 0,
      "replies": [
        {
          "id": 1479165,
          "author_name": "CPMP",
          "author_url": "",
          "post_date": "2021-08-18T10:17:27.143000",
          "content": "<p>Host said waves are shifted in this simulation depending on the simulated position of wave origin.</p>",
          "votes": 3,
          "replies": []
        }
      ]
    },
    {
      "id": 1384120,
      "author_name": "Nerthiga Balakrishnan",
      "author_url": "",
      "post_date": "2021-07-11T13:23:51.540000",
      "content": "<p>Very interesting!</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1382456,
      "author_name": "Haruhiko Kondo",
      "author_url": "",
      "post_date": "2021-07-09T21:55:28.397000",
      "content": "<p>Thank you so much for the very useful information, indeed.</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1377913,
      "author_name": "Pranesh Rajendra Kumar",
      "author_url": "",
      "post_date": "2021-07-06T08:01:53.237000",
      "content": "<p>Amazing work, and its really helpful when I struggle. Appreciate your work! <a href=\"https://www.kaggle.com/crained\" target=\"_blank\">@crained</a> </p>",
      "votes": 0,
      "replies": [
        {
          "id": 1378231,
          "author_name": "Charlie Craine",
          "author_url": "",
          "post_date": "2021-07-06T11:45:43.713000",
          "content": "<p>Thank you <a href=\"https://www.kaggle.com/praneshrajendrakumar\" target=\"_blank\">@praneshrajendrakumar</a> hope they help!</p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 1377901,
      "author_name": "krishnaprasad",
      "author_url": "",
      "post_date": "2021-07-06T07:50:14.913000",
      "content": "<p>Thanks for the post, saved a lot of time.</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1374213,
      "author_name": "Adhithia",
      "author_url": "",
      "post_date": "2021-07-03T05:57:11.523000",
      "content": "<p>This was very insightful. Thank you! <a href=\"https://www.kaggle.com/crained\" target=\"_blank\">@crained</a> </p>",
      "votes": 0,
      "replies": [
        {
          "id": 1374499,
          "author_name": "Charlie Craine",
          "author_url": "",
          "post_date": "2021-07-03T11:16:25.723000",
          "content": "<p>Thanks <a href=\"https://www.kaggle.com/adhithia\" target=\"_blank\">@adhithia</a> good luck with the competition!</p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 1372408,
      "author_name": "Omkar Mule",
      "author_url": "",
      "post_date": "2021-07-01T17:02:06.240000",
      "content": "<p>Very useful information. I will try to add any papers if I find it.</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1371305,
      "author_name": "WOOSUNG YOON",
      "author_url": "",
      "post_date": "2021-06-30T21:20:47.313000",
      "content": "<p>Thank you. This will be of great help.</p>",
      "votes": 0,
      "replies": [
        {
          "id": 1371405,
          "author_name": "Charlie Craine",
          "author_url": "",
          "post_date": "2021-07-01T01:20:40.893000",
          "content": "<p><a href=\"https://www.kaggle.com/woosungyoon\" target=\"_blank\">@woosungyoon</a> I hope it helps you in the competition!</p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 1371272,
      "author_name": "Arpit Verma",
      "author_url": "",
      "post_date": "2021-06-30T20:11:38.767000",
      "content": "<p>Very helpful, thanks!</p>",
      "votes": 0,
      "replies": [
        {
          "id": 1371406,
          "author_name": "Charlie Craine",
          "author_url": "",
          "post_date": "2021-07-01T01:21:04.397000",
          "content": "<p>No problem <a href=\"https://www.kaggle.com/varpit94\" target=\"_blank\">@varpit94</a> … good luck on the competition. </p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 1398386,
      "author_name": "",
      "author_url": "",
      "post_date": "2021-07-24T05:49:30.667000",
      "content": "",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1371494,
      "author_name": "Samarth Gupta",
      "author_url": "",
      "post_date": "2021-07-01T03:58:58.333000",
      "content": "<p>Very helpful, Thanks for sharing🙌</p>",
      "votes": 1,
      "replies": []
    },
    {
      "id": 1406073,
      "author_name": "Joonsu Oh",
      "author_url": "",
      "post_date": "2021-07-31T13:20:06.407000",
      "content": "<p>Thanks a lot!</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1395170,
      "author_name": "Saurav Maheshkar ☕️",
      "author_url": "",
      "post_date": "2021-07-21T01:50:34.243000",
      "content": "<p>Thanks you for sharing </p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1383083,
      "author_name": "RenJunJie_XDU",
      "author_url": "",
      "post_date": "2021-07-10T13:50:06.540000",
      "content": "<p>Thank you.</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1377358,
      "author_name": "Rohan Paul",
      "author_url": "",
      "post_date": "2021-07-05T19:06:41.910000",
      "content": "<p>Thank you so much for compiling it. 👋</p>",
      "votes": 0,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1371150": "Hey everyone!\n\nI wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\n**Research Papers:**\n\n- [Accelerating Recurrent Neural Networks for Gravitational Wave Experiments](https://arxiv.org/abs/2106.14089) - This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such as black hole mergers which happen at unknown times and of varying durations, producing time-series data. We have developed a new architecture capable of accelerating RNN inference for analyzing time-series data from LIGO detectors. \n\n- [Inference with finite time series: Observing the gravitational Universe through windows](https://arxiv.org/abs/2106.13785) - Time series analysis is ubiquitous in many fields of science including gravitational-wave astronomy, where strain time series are analyzed to infer the nature of gravitational-wave sources, e.g., black holes and neutron stars. It is common in gravitational-wave transient studies to apply a tapered window function to reduce the effects of spectral artifacts from the sharp edges of data segments. We show that the conventional analysis of tapered data fails to take into account covariance between frequency bins, which arises for all finite time series -- no matter the choice of window function. \n\n- [Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning](https://arxiv.org/abs/2106.12466) - A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pairs. As a proof of principle, we simulate non-spinning binary black hole events added to Gaussian noise, and train the machine on their time-frequency maps (Q-transforms) and localisation skymaps (using Bayestar), both of which can be generated in seconds. We show that the trained machine is able to accurately identify lensed pairs with efficiencies comparable to existing Bayesian methods.\n\n- [Real-time gravitational-wave science with neural posterior estimation](https://arxiv.org/abs/2106.12594) - We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog and find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.\n\n- [The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors](https://arxiv.org/abs/2106.13769) - The next generation of gravitational-wave experiments, such as Einstein Telescope, Cosmic Explorer and LISA, will test the primordial black hole scenario. We provide a forecast for the minimum testable value of the abundance of primordial black holes as a function of their masses for both the unclustered and clustered spatial distributions at formation. In particular, we show that these instruments may test abundances, relative to the dark matter, as low as 10−10.\n\n- [Memory Effects in Scattering from Accelerating Bodies](https://arxiv.org/abs/2106.14194) - Here we investigate theoretically and experimentally an interaction regime, which is neither relativistic nor adiabatic. The test model considers an accelerating scatterer with a long-lasting relaxation memory. The slow decay rates violate the instantaneous reaction assumption of quasi-stationarity, introducing non-Markovian contributions to the scattering process. Memory signatures in scattering from a rotating dipole are studied theoretically, showing symmetry breaking of micro-Doppler combs. A quasi-stationary numeric analysis of scattering in the short memory limit is proposed and validated experimentally with an example of electromagnetic pulses interacting with a rotating wire.\n\n- [Observation of gravitational waves from two neutron star-black hole coalescences](https://arxiv.org/abs/2106.15163) - We report the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run with properties consistent with neutron star-black hole (NSBH) binaries.",
    "1375086": "This one might be relevant:\n\n[**A guide to LIGO-Virgo detector noise and\nextraction of transient gravitational-wave signals**](https://arxiv.org/abs/1908.11170)\n\n\"...In this paper, we provide an overview of the detector noise properties\nand the data analysis techniques used to detect gravitational-wave signals and\ninfer the source properties. We describe some of the checks that are performed\nto validate the analyses and results from the observations of gravitational-wave\nevents. We also address concerns that have been raised about various properties\nof LIGO-Virgo detector noise and the correctness of our analyses as applied to the\nresulting data.\"",
    "1371409": "I found another:\n[Enhancing gravitational-wave science with machine learning](https://iopscience.iop.org/article/10.1088/2632-2153/abb93a) - Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.",
    "1372017": "Is it better for me to add to the main list or add in comments? I found four more recent papers!",
    "1561215": "Hey All,\n\nThank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey https://forms.gle/QP9L16niPexozyhu5.\n\nThank you all,\n\nRegards,\nChris",
    "1467585": "Hi:\nI am confusing about one question in [https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102](url):\nIn real world, the actual gravitational wave （GW150914） arrived first at L1 and 6.9 millisecond later at H1 (to account for the detectors’ relative orientations), even though Gravitational Waves travel at the speed of light.\nAnd I am puzzled that is simulated gravitational wave signal arrive at different time too (shifted before plus to different noise)? Or it was plus to noisy directly without shifted?\nWelcome to discuss!!!",
    "1384120": "Very interesting!",
    "1382456": "Thank you so much for the very useful information, indeed.",
    "1377913": "Amazing work, and its really helpful when I struggle. Appreciate your work! @crained ",
    "1377901": "Thanks for the post, saved a lot of time.",
    "1374213": "This was very insightful. Thank you! @crained ",
    "1372408": "Very useful information. I will try to add any papers if I find it.\n",
    "1371305": "Thank you. This will be of great help.",
    "1371272": "Very helpful, thanks!",
    "1398386": "",
    "1371494": "Very helpful, Thanks for sharing🙌",
    "1406073": "Thanks a lot!",
    "1395170": "Thanks you for sharing ",
    "1383083": "Thank you.",
    "1377358": "Thank you so much for compiling it. 👋"
  }
}