Hello everyone!
\nI wanted to start a thread about articles and expand it, and hopefully others will share as well, articles to gain knowledge in the field. I have no knowledge in this area, so I downloaded the articles that seemed relevant after reading their summaries.
\nPapers:
\nDeep-Learning Continuous Gravitational Waves - We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals. We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline. As test benchmarks we consider two types of all-sky searches over the frequency range from 20Hz to 1000Hz: an 'easy' search using T=105s of data, and a 'harder' search using T=106s. Detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105s at 20Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher-frequencies and longer observation time the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106s at 1000Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.
Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data - The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.
Enhancing gravitational-wave science with machine learning - 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.
Deep learning for gravitational wave forecasting of neutron star mergers - We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.
Resources
\nGravitational Wave Data Analysis with Machine Learning - 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 Zotero group.
G2Net Gravitational Wave Detection (2021)
\n\nHave a good competition and don't hesitate to comment!
", "messages": [ { "id": 1972509, "postDate": "2022-10-05T07:29:43.283Z", "content": "Hello everyone!
\nI wanted to start a thread about articles and expand it, and hopefully others will share as well, articles to gain knowledge in the field. I have no knowledge in this area, so I downloaded the articles that seemed relevant after reading their summaries.
\nPapers:
\nDeep-Learning Continuous Gravitational Waves - We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals. We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline. As test benchmarks we consider two types of all-sky searches over the frequency range from 20Hz to 1000Hz: an 'easy' search using T=105s of data, and a 'harder' search using T=106s. Detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105s at 20Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher-frequencies and longer observation time the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106s at 1000Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.
Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data - The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.
Enhancing gravitational-wave science with machine learning - 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.
Deep learning for gravitational wave forecasting of neutron star mergers - We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.
Resources
\nGravitational Wave Data Analysis with Machine Learning - 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 Zotero group.
G2Net Gravitational Wave Detection (2021)
\n\nHave a good competition and don't hesitate to comment!
", "rawMarkdown": "Hello everyone!\n\nI wanted to start a thread about articles and expand it, and hopefully others will share as well, articles to gain knowledge in the field. I have no knowledge in this area, so I downloaded the articles that seemed relevant after reading their summaries.\n\n**Papers:**\n\n- [Deep-Learning Continuous Gravitational Waves](https://arxiv.org/abs/1904.13291) - We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals. We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline. As test benchmarks we consider two types of all-sky searches over the frequency range from 20Hz to 1000Hz: an 'easy' search using T=105s of data, and a 'harder' search using T=106s. Detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105s at 20Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher-frequencies and longer observation time the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106s at 1000Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search. \n\n- [Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data](https://www.sciencedirect.com/science/article/pii/S0370269317310390) - The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.\n\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.\n\n- [Deep learning for gravitational wave forecasting of neutron star mergers](https://www.sciencedirect.com/science/article/pii/S0370269321001258) - We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.\n\n**Resources**\n\n- [Introduction to LIGO & Gravitational Waves](https://www.ligo.org/science/GW-Continuous.php)\n\n- [Gravitational Wave Data Analysis with Machine Learning](https://iphysresearch.github.io/Survey4GWML/) - 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 Zotero group.\n\n**G2Net Gravitational Wave Detection (2021)**\n\n- [Papers on Gravitational Wave Detection and Machine Learning](https://www.kaggle.com/competitions/g2net-gravitational-wave-detection/discussion/249993)\n- [Research Papers to get started](https://www.kaggle.com/competitions/g2net-gravitational-wave-detection/discussion/249991)\n\n**Have a good competition and don't hesitate to comment!**", "votes": 33 }, { "id": 2022389, "postDate": "2022-11-09T02:40:39.313Z", "content": "Thanks for the references!
", "rawMarkdown": "Thanks for the references!" } ], "comments": [ { "id": 2022389, "author_name": "Erick Almaraz", "author_url": "", "post_date": "2022-11-09T02:40:39.313000", "content": "Thanks for the references!
", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "1972509": "Hello everyone!\n\nI wanted to start a thread about articles and expand it, and hopefully others will share as well, articles to gain knowledge in the field. I have no knowledge in this area, so I downloaded the articles that seemed relevant after reading their summaries.\n\n**Papers:**\n\n- [Deep-Learning Continuous Gravitational Waves](https://arxiv.org/abs/1904.13291) - We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals. We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline. As test benchmarks we consider two types of all-sky searches over the frequency range from 20Hz to 1000Hz: an 'easy' search using T=105s of data, and a 'harder' search using T=106s. Detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105s at 20Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher-frequencies and longer observation time the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106s at 1000Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search. \n\n- [Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data](https://www.sciencedirect.com/science/article/pii/S0370269317310390) - The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.\n\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.\n\n- [Deep learning for gravitational wave forecasting of neutron star mergers](https://www.sciencedirect.com/science/article/pii/S0370269321001258) - We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.\n\n**Resources**\n\n- [Introduction to LIGO & Gravitational Waves](https://www.ligo.org/science/GW-Continuous.php)\n\n- [Gravitational Wave Data Analysis with Machine Learning](https://iphysresearch.github.io/Survey4GWML/) - 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 Zotero group.\n\n**G2Net Gravitational Wave Detection (2021)**\n\n- [Papers on Gravitational Wave Detection and Machine Learning](https://www.kaggle.com/competitions/g2net-gravitational-wave-detection/discussion/249993)\n- [Research Papers to get started](https://www.kaggle.com/competitions/g2net-gravitational-wave-detection/discussion/249991)\n\n**Have a good competition and don't hesitate to comment!**", "2022389": "Thanks for the references!" } }