Gravitational 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.

MLA Web (private access)
G2Net Gravitational Wave Detection (Kaggle data science competation)
G2Net Detecting Continuous Gravitational Waves (Kaggle data science competation)
NSF - Mathematical Sciences Institutes (video)
FastML Lab - Real-time and accelerated ML for fundamental sciences
Accelerated AI Algorithms for Data-Driven Discovery (A3D3) (Youtube channel)
NSF HDR A3D3: Detecting Anomalous Gravitational Wave Signals (Codabench competition)

1. Conferences & Workshops¶

The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) - Events - (Home)
The IST seminar series Mathematics, Physics & Machine Learning - $(M \cup \Phi) \cap M L$ - (Home)
Physics Meets ML - $\text{Physics} \cap \text{ML}$ - (Home)
Community Laboratory for AI Research at the Intersection with Physics (CLARIPHY) - Topical Meetings - (Home)
Laboratory for Artificial Intelligence for Scientific Discovery - (Home)
(Dec 8, 2017) - Deep Learning for Physical Sciences (workshop at NeurIPS)
(Oct 17, 2018) - Deep Learning for Multimessenger Astrophysics: Real-time Discovery at Scale
(Sep 10, 2019) - Fast Machine Learning Workshop at Fermi National Accelerator Laboratory
(Dec 14, 2019) - Machine Learning and the Physical Sciences (workshop at NeurIPS)
(March 9, 2020) - CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning - 2nd Training School (G2NET)
(April 21, 2020) - Machine Learning for Physicists 2020
(19 Oct, 2020) - 2020 Accelerated Artificial Intelligence for Big-Data Experiments Conference
(Sep 9, 2020) - Advances in Computational Relativity
(Oct 19, 2020) - 2020 Accelerated Artificial Intelligence for Big-Data Experiments Conference
(Nov 16, 2020) - Statistical Methods for the Detection, Classification, and Inference of Relativistic Objects
(Dec 12, 2020) - Interpretable Inductive Biases and Physically Structured Learning (workshop at NeurIPS)
(May 27, 2020) - Ellis Fellows Program Quantum and Physics based Machine Learning (QPhML) (2021, 2020, 2019)
(Dec 13, 2021) - Machine Learning and the Physical Sciences (2021, 2020) (workshop at NeurIPS)
(Dec 6, 2021) - AI for Science: Mind the Gaps (NeurIPS 2021)
(Nov 14, 2021) - Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy: From Mathematical Modelling to Machine Learning (21w5066)
(Nov 29, 2021) - Workshop IV: Big Data in Multi-Messenger Astrophysics (Part of the Long Program Mathematical and Computational Challenges in the Era of Gravitational Wave Astronomy)
(July 23, 2022) - AI for Science: Theories and Foundations (ICML 2022)
(July 22, 2022) - Machine Learning for Astrophysics (ICML 2022)
(August 1-9, 2022) - IAIFI Summer School & Workshop, 2022 | Recap
(September 28-30, 2022) - Machine Learning in GW search: g2net next challenges
(Nov 21-25, 2022) - LISA data analysis: from classical methods to machine learning
(Dec 2, 2022) - AI for Science: Progress and Promises (NeurIPS 2022)
(January 28, 2023) - Accelerating Physics with ML@MIT
(March 28-30, 2023) - G2Net - A network for Gravitational Waves, Geophysics and Machine Learning (CA17137)
(July 28, 2023) - Machine Learning for Astrophysics (ICML 2023)
(May 22, 2023) - Cosmic Connections: A ML X Astrophysics Symposium at Simons Foundation
(Dec 15, 2023) - AI for Scientific Discovery: From Theory to Practice (NeurIPS 2023)
(April 23, 2024) - The 2024 AIslands meeting focusing on artificial intelligence in gravitational wave astronomy.
(June 17, 2024) - AstroAI Workshop - Unveiling the Universe with AI/ML
(Nov 17, 2024) - Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy (24w5177)
(June 2, 2025) - Scientific Machine Learning for Gravitational Wave Astronomy (ICERM)

2. General Reports & Reviews¶

Modern deep learning methods have entered the field of physics which can be tasked with learning physics from raw data when no good mathematical models are available. 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 can’t incorporate all details (think noise).

[Wong (2023)] - MACHINE LEARNING-ENHANCED ANALYSIS IN GW (slide)
[Agarwal et al. (2023) ¹ (2306.08106)] - Applications of Deep Learning to Physics Workflows
[Huerta et al. (2022) ² (Scientific Data)] - FAIR for AI: An Interdisciplinary, International, Inclusive, and Diverse Community Building Perspective
[Cuoco et al. (2022) ³ (Nature Computational Science)] - Computational Challenges for Multimodal Astrophysics
[Ravi et al. (2022) ⁴ (Scientific Data)] - FAIR Principles for AI Models, with a Practical Application for Accelerated High Energy Diffraction Microscopy
[Gunny et al. (2022) [@10.1145/3526058.3535454] (ACM)] - A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics
[Harris et al. (2022) ⁵ (2203.16255)] - Physics Community Needs, Tools, and Resources for Machine Learning
[Deiana et al. (2022) ⁶ ( Front. big data)] - Applications and Techniques for Fast Machine Learning in Science
[Dvorkin et al. (2022) ⁷ (2203.08056)] - Machine Learning and Cosmology
[Smith (2021) ⁸ (Nature Physics)] - OK Computer
[Ntampaka et al. (2021) ⁹ (2111.14566)] - Building Trustworthy Machine Learning Models for Astronomy
[Huerta & Zhao (2021) ¹⁰ (Handbook of Gravitational Wave Astronomy)] - Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-messenger Sources
[Agrawal et al. (2020) ¹¹ (Springer Singapore)] - Machine Learning Based Analysis of Gravitational Waves
[Huerta et al. (2021) ¹² (Nature Astronomy)] - Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
[Coughlin (2020) ¹³ (Nature Astronomy)] - Lessons from Counterpart Searches in LIGO and Virgo’s Third Observing Campaign
[Zdeborová (2020) ¹⁴ (Nature Physics)] - Understanding Deep Learning Is Also a Job for Physicists
[Cuoco et al. (2020) ¹⁵ (Mach. learn.: sci. technol.)] - Enhancing Gravitational-wave Science with Machine Learning
[Huerta et al. (2020) ¹⁶ (J. Big Data)] - Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
[Fluke et al. (2020) ¹⁷ (WIRDMKD)] - Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
Viewpoint: Machine Learning Tackles Spacetime By Enrico Rinaldi March 23, 2020 - Physics 13, 40
[Huerta et al. (2019) ¹⁸ (CSBS)] - Supporting High-Performance and High-Throughput Computing for Experimental Science
[Huerta et al. (2019) ¹⁹ (Nature Reviews Physics)] - Enabling Real-Time Multi-Messenger Astrophysics Discoveries with Deep Learning
[Allen et al. (2019) ²⁰ (1902.00522)] - Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
[Eadie et al. (2019) ²¹ (1909.11714)] - Realizing the Potential of Astrostatistics and Astroinformatics
[Foley et al. (2019) ²² (1903.04553)] - Gravity and Light-Combining Gravitational Wave and Electromagnetic Observations in the 2020s

3. Improving Data Quality¶

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 [Lightman et al. (2006) ²³ (JPCS)] to the study of glitches [Essick et al. (2013) ²⁴ (CQG); Biswas et al. (2013) ²⁵ (PRD)] and other problems, such as signal characterization [Baker et al. (2015) ²⁶ (PRD)] . For example, Gstlal-iDQ [Vaulin et al. (2013) ²⁷] (a streaming machine learning pipeline based on [Essick et al. (2013) ²⁴ (CQG)] and [Biswas et al. (2013) ²⁵ (PRD)] reported the probability that there was a glitch in $h(t)$ 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.

Glitch Classification¶

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 [Davis et al. (2020) ²⁸ (CQG)].

PCA based
- Early ML studies for glitch classification used Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM). (See [Powell et al. (2015) ²⁹ (CQG)] test on simulated data & [Powell et al. (2017) ³⁰ (CQG)] 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 [Powell (2017) ³¹ (PhD Thesis); Cuoco (2018) ³² (Workshop)]
- 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 $D$ is factored so that $D= U\Sigma V^T$ where $V=A^TA$ , $\Sigma$ contains eigenvalues, and $U$ 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.
CNN (Images feature)
- [Razzano & Cuoco 2018 ³³ (CQG)] 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 ≈98-99% on multiclass classification
- [George et al. (2018) ³⁴ (PRD)]: “Deep 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.”
Wavelet-based (Time series feature)
- [Cuoco et al. (2018) ³⁵ (IEEE)] - Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors
GravitySpy Project
- GravitySpy [Zevin et al. (2017) ³⁶ (CQG); Coughlin et al. (2019) ³⁷ (PRD)] uses citizen scientists to produce training sets for machine learning glitch classification.
- (How do I try it myself?) Log into gravityspy.org to try classifying glitches. Download already labelled LIGO glitches for training your algorithm from zenodo One / Two.
- [Soni et al. (2021) ³⁸ (CQG)] - Discovering Features in Gravitational-wave Data through Detector Characterization, Citizen Science and Machine Learning
Others / Pending:
- [Staats & Cavaglià (2018) ³⁹ (Commun. Comput. Phys.)] - Finding the origin of noise transients in LIGO data with machine learning (Karoo GP)
- [Mukund et al. (2017) ⁴⁰ (PRD)] - Transient classification in LIGO data using difference boosting neural network (Wavelet-DBNN, India)
- [Llorens-Monteagudo et al. (2019) ⁴¹ (CQG)] - Classification of gravitational-wave glitches via dictionary learning (Dictionary learning)
- Low latency transient detection and classification (I. Pinto, V. Pierro, L. Troiano, E. Mejuto-Villa, V. Matta, P. Addesso)
- [George et al. (2018) ³⁴ (PRD)] - Classification and unsupervised clustering of LIGO data with Deep Transfer Learning (Deep Transfer Learning)
- [Astone et al. (2018) ⁴² (PRD)] - New method to observe gravitational waves emitted by core collapse supernovae (RGB image SN CNN)
- [Colgan et al. (2020) ⁴³ (PRD)] - Efficient gravitational-wave glitch identification from environmental data through machine learning
- [Bahaadini et al. (2017) ⁴⁴ (IEEE)] - Deep Multi-View Models for Glitch Classification
- [Bahaadini et al. (2018) ⁴⁵ (Info. Sci.)] - Machine learning for Gravity Spy: Glitch classification and dataset
- [Bahaadini et al. (2018) ⁴⁶ (IEEE)] - DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
- Young-Min Kim - Noise Identification in Gravitational wave search using Artificial Neural Networks (PDF) (4th K-J workshop on KAGRA @ Osaka Univ.)
- [Biswas et al. (2020) ⁴⁷ (CQG)] - New Methods to Assess and Improve LIGO Detector Duty Cycle
- [Morales-Alvarez et al. (2020) ⁴⁸ (IEEE)] - Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
- [Marianer et al. (2020) ⁴⁹ (Mon. Not. Roy. Astron. Soc.)] - A Semisupervised Machine Learning Search for Never-seen Gravitational-wave Sources
- [Mesuga & Bayanay (2021) ⁵⁰ (2107.01863)] - On the Efficiency of Various Deep Transfer Learning Models in Glitch Waveform Detection in Gravitational-wave Data
- [Sankarapandian & Kulis (2021) ⁵¹ (2107.10667)] - $β$ -Annealed Variational Autoencoder for Glitches
- [Yu & Adhikari (2021) ⁵² (2111.03295)] - Nonlinear Noise Regression in Gravitational-Wave Detectors with Convolutional Neural Networks
- [Sakai et al. (2021) ⁵³ (2111.10053)] - Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors
- [Merritt et al. (2021) ⁵⁴ (PRD)] - Transient Glitch Mitigation in Advanced LIGO Data
- [Colgan et al. (2022) ⁵⁵ (2202.13486)] - Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
- [Davis et al. (2022) ⁵⁶ (2204.03091)] - Incorporating Information from LIGO Data Quality Streams into the PyCBC Search for Gravitational Waves
- [Bahaadini et al. (2022) ⁵⁷ (2205.13672)] - Discriminative Dimensionality Reduction Using Deep Neural Networks for Clustering of LIGO Data
- [Yan et al. (2022) ⁵⁸ (Mon. Not. Roy. Astron. Soc.)] - On Improving the Performance of Glitch Classification for Gravitational Wave Detection by Using Generative Adversarial Networks
- [Sakai et al. (2022) ⁵⁹ (2208.03623)] - Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset
- [Powell et al. (2022) ⁶⁰ (CQG)] Generating Transient Noise Artifacts in Gravitational-Wave Detector Data with Generative Adversarial Networks
- [Iqbal (2022) ⁶¹ (ResearchGate)] - Classifying the Gravitational Waves Using the Deep Learning Technique
- [Ferreira et al. (2022) ⁶² (CQG)] - Comparison between T-SNE and Cosine Similarity for LIGO Glitches Analysis
- [Dooney et al. (2022) ⁶³ (2209.13592)] - DVGAN: Stabilize Wasserstein GAN Training for Time-Domain Gravitational Wave Physics
- [Boudart et al. (2022) ⁶⁴ (2210.04588)] - A Convolutional Neural Network to Distinguish Glitches from Minute-Long Gravitational Wave Transients
- [Bişkin et al. (2022) ⁶⁵ (Astron. Comput.)] - A Fast and Time-Efficient Glitch Classification Method: A Deep Learning-Based Visual Feature Extractor for Machine Learning Algorithms
- [Ruiz et al. (2022) ⁶⁶ (KBS)] - Probabilistic Fusion of Crowds and Experts for the Search of Gravitational Waves
- [Razzano et al. (2022) ⁶⁷ (Nucl. Instrum.)] - GWitchHunters: Machine Learning and Citizen Science to Improve the Performance of Gravitational Wave Detector
- [Bini et al. (2023) ⁶⁸ (2303.05986)] - An Autoencoder Neural Network Integrated into Gravitational-Wave Burst Searches to Improve the Rejection of Noise Transients
- [Fernandes et al. (2023) ⁶⁹ (2303.13917)] - Convolutional Neural Networks for the Classification of Glitches in Gravitational-Wave Data Streams
- [Alvarez-Lopez et al. (2023) [@2023Alvarez-LopezGSpyNetTreesignalvsglitchclassifier] (2304.09977)] - GSpyNetTree: A Signal-vs-Glitch Classifier for Gravitational-Wave Event Candidates
- [Shah et al. (2023) ⁷⁰ (2306.13787)] - Waves in a Forest: A Random Forest Classifier to Distinguish between Gravitational Waves and Detector Glitches
- [Raza et al. (2023) ⁷¹ (2308.12357)] - Explaining the GWSkyNet-Multi Machine Learning Classifier Predictions for Gravitational-Wave Events

Glitch cancellation / GW denosing¶

Pending:
- [Cuoco et al. (2001) ⁷² (CQG)] - On-line power spectra identification and whitening for the noise in interferometric gravitational wave detectors
- [Torres-Forné (2016) ⁷³ (PRD)] - Denoising of Gravitational Wave Signals Via Dictionary Learning Algorithms
- [Torres et al. (2014) [@2014TorresTotalvariationbasedmethodsgravitational] (PRD)] - Total-Variation-Based Methods for Gravitational Wave Denoising
- [Torres-Forné (2018) [@torres2018total] (PRD)] - Total-variation methods for gravitational-wave denoising: Performance tests on Advanced LIGO data
- [Torres-Forné (2020) ⁷⁴ (PRD)] - Application of dictionary learning to denoise LIGO’s blip noise transients
- [Shen et al. (2019) [@Shen2019ohi] (IEEE)] - Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-encoders
- [Wei & Huerta (2020) [@2020WeiGravitationalwavedenoising] (PLB)] - Gravitational wave denoising of binary black hole mergers with deep learning
- [Vajente et al. (2020) [@Vajente2019ycy] (PRD)] - Machine-learning nonstationary noise out of gravitational-wave detectors
- [Alimohammadi et al. (2021) ⁷⁵ (Scientific Reports)] - A Template-Free Approach for Waveform Extraction of Gravitational Wave Events
- [Ormiston et al. (2020) ⁷⁶ (PRR)] - Noise Reduction in Gravitational-Wave Data via Deep Learning
- [Essick et al. (2020) ⁷⁷ (Mach. learn.: sci. technol.)] - iDQ: Statistical Inference of Non-gaussian Noise with Auxiliary Degrees of Freedom in Gravitational-wave Detectors
- [Mogushi et al. (2021) [@2021MogushiNNETFIXartificialneural] (Mach. learn.: sci. technol.)] - NNETFIX: an artificial neural network-based denoising engine for gravitational-wave signals
- [Chatterjee et al. (2021) [@2021ChatterjeeExtractionBinaryBlack] (PRD)] - Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning
- [Mogushi (2021) ⁷⁸ (2105.10522)] - Reduction of Transient Noise Artifacts in Gravitational-wave Data Using Deep Learning
- [Colgan et al. (2022) ⁷⁹ (2203.05086)] - Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
- [Lopez et al. (2022) [@2022LopezSimulatingTransientNoise] (2203.06494)] - Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
- [Yu & Adhikari (2022) ⁸⁰ (Front. Artif. Intell.)] - Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks
- [Lopez et al. (2022) [@2022LopezSimulatingTransientNoisea] (2205.09204)] - Simulating Transient Noise Bursts in LIGO with Gengli
- [Vajente (2022) [@PhysRevD.105.102005] (PRD)] - Data Mining and Machine Learning Improve Gravitational-Wave Detector Sensitivity
- [Bacon et al. (2022) [@2022BaconDenoisinggravitationalwavesignals] (2205.13513)] - Denoising Gravitational-Wave Signals from Binary Black Holes with Dilated Convolutional Autoencoder
- [Kato et al. (2022) ⁸¹ (Astron. Comput.)] - Validation of Denoising System Using Non-Harmonic Analysis and Denoising Convolutional Neural Network for Removal of Gaussian Noise from Gravitational Waves Observed by LIGO
- [Ashton (2022) ⁸² (2209.15547)] - Gaussian Processes for Gravitational-wave Astronomy
- [Murali & Lumley (2022) ⁸³ (2210.01718)] - Detecting and Denoising Gravitational Wave Signals from Binary Black Holes Using Deep Learning
- [Yang et al. (2023) ⁸⁴ (2305.06735)] - Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on Noise2Noise Method
- [Saleem et al. (2023) [@2023SaleemDemonstrationMachineLearningassisted] (2306.11366)] - Demonstration of Machine Learning-assisted Real-Time Noise Regression in Gravitational Wave Detectors

4. Compact Binary Coalesces (CBC)¶

Waveform Modelling¶

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, [Docter et al. (2017) ⁸⁵ (PRD)]

Machine Learning for waveform generation: Use optimal waveform generators, i.e., machine learning models trained with numerical relativity waveforms [Blackman et al. (2017) ⁸⁶ (PRD); Huerta et al. (2018) ⁸⁷ (PRD)] (For eccentric black hole mergers [Varma et al. (2019) ⁸⁸ (PRD); Varma et al. (2019) ⁸⁹ (PRR)])
Pending:
- [Chua (2017) ⁹⁰ (PhD Thesis)] - Topics in gravitational-wave astronomy: Theoretical studies, source modelling and statistical methods
- [Chua et al. (2019) ⁹¹ (PRL)] - Reduced-oruder modeling with artificial neurons for gravitational-wave inference
- [Setyawati et al. (2020) [@2020SetyawatiRegressionmethodswaveform] (CQG)] - Regression Methods in Waveform Modeling: A Comparative Study
- [Tiglio & Villanueva (2019) [@2021TiglioAbInitiobased] (Scientific Reports)] - On Ab Initio-based, Free and Closed-form Expressions for Gravitational Waves
- [Schmidt (2019) ⁹² (Masters Thesis)] - Gravitational Wave Modelling with Machine Lerning
- [Chen et al. (2020) ⁹³ (2008.03313)] - Observation of Eccentric Binary Black Hole Mergers with Second and Third Generation Gravitational Wave Detector Networks
- [Khan & Green (2020) ⁹⁴ (PRD)] - Gravitational-wave Surrogate Models Powered by Artificial Neural Networks
- [Schmidt et al. (2020) ⁹⁵ (PRD)] - Machine Learning Gravitational Waves from Binary Black Hole Mergers
- [Lee et al. (2021) ⁹⁶ (PRD)] - Deep Learning Model on Gravitational Waveforms in Merging and Ringdown Phases of Binary Black Hole Coalescences
- [Liao & Lin (2021) ⁹⁷ (PRD)] - Deep Generative Models of Gravitational Waveforms via Conditional Autoencoder
- [Chua et al. (2021) ⁹⁸ (PRL)] - Rapid Generation of Fully Relativistic Extreme-mass-ratio-inspiral Waveform Templates for LISA Data Analysis
- [Keith et al. (2021) ⁹⁹ (PRR)] - Orbital Dynamics of Binary Black Hole Systems Can Be Learned from Gravitational Wave Measurements
- [McGinn et al. (2021) ¹⁰⁰ (CQG)] - Generalised Gravitational Burst Generation with Generative Adversarial Networks
- [Nousi et al. (2021) ¹⁰¹ (2107.04312)]
- [Barsotti et al. (2021) ¹⁰² (CQG)] - Gravitational Wave Surrogates through Automated Machine Learning
- [Khan et al. (2022) ¹⁰³ (PRD)] - Interpretable AI Forecasting for Numerical Relativity Waveforms of Quasicircular, Spinning, Nonprecessing Binary Black Hole Mergers
- [Coogan et al. (2022) ¹⁰⁴ (PRD)] - Efficient Gravitational Wave Template Bank Generation with Differentiable Waveforms
- [Freitas et al. (2022) ¹⁰⁵ (2203.01267)] - Generating Gravitational Waveform Libraries of Exotic Compact Binaries with Deep Learning
- [Freitas et al. (2022) ¹⁰⁶ (2203.08434)] - Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling
- [Thomas et al. (2022) ¹⁰⁷ (2205.14066)] - Accelerating Multimodal Gravitational Waveforms from Precessing Compact Binaries with Artificial Neural Networks
- [Ferguson (2022) ¹⁰⁸ (PRD)] - Optimizing the Placement of Numerical Relativity Simulations Using a Mismatch Predicting Neural Network
- [Tissino et al. (2022) ¹⁰⁹ (2210.15684)] - Combining Effective-One-Body Accuracy and Reduced-Order-Quadrature Speed for Binary Neutron Star Merger Parameter Estimation with Machine Learning
- [Pereira & Sturani (2022) ¹¹⁰ (2210.07299)] - Deep Learning Waveform Anomaly Detector for Numerical Relativity Catalogs
- [Katz et al. (2022) ¹¹¹ (PRD)] - Fast Extreme-Mass-Ratio-Inspiral Waveforms: New Tools for Millihertz Gravitational-Wave Data Analysis

Signal Detection (BBHs)¶

Matched filter searches:
- Searches for gravitational wave signals from compact binaries use matched filtering. GW detector noise is non-Stationary and non-Gaussian.
- 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.
- 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;
- ML searches can help if they are fast and do as well on detector noise artefacts.
Machine learning CBC searches:

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;
- [George & Huerta (2018) ¹¹² (PRD)] 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.
  - Deep learning for real-time classification and regression of gravitational waves in simulated LIGO noise. [George & Huerta (2018) ¹¹² (PRD)]
  - Deep learning for real-time classification and regression of gravitational waves in real advanced LIGO noise. [George & Huerta (2018) ¹¹³ (PLB) ¹¹⁴ (1711.07966); George & Huerta (2018) [@George2017vlv] (NiPS Summer School)]
  - Deep learning at scale for real-time gravitational wave parameter estimation and tests of general relativity [Shen et al. (2019) ¹¹⁵ (Mach. learn.: sci. technol.)]. 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.
- [Gabbard et al. (2018) ¹¹⁶ (PRL)] 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 et al. (2020) ¹¹⁷ (Front. Phys.)] - Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
- [Kapadia et al. (2017) ¹¹⁸ (PRD)] - Classifier for Gravitational-wave Inspiral Signals in Nonideal Single-detector Data
- [Cao et al. (2018) ¹¹⁹ (JHNU)] - Initial study on the application of deep learning to the Gravitational Wave data analysis
- [Fan et al. (2019) ¹²⁰ (SCI CHINA PHYS MECH)] - Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
- [Luo et al. (2019) ¹²¹ (Front. Phys.)] - Extraction of gravitational wave signals with optimized convolutional neural network
- [Lin et al. (2019) ¹²² (Front. Phys.)] - Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis and Convolutional Neural Networks
- [Wang et al. (2019) ¹²³ (New J. Phys.)] - Identifying Extra High Frequency Gravitational Waves Generated from Oscillons with Cuspy Potentials Using Deep Neural Networks
- [Rebei et al. (2019) ¹²⁴ (PRD)] - Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
- [Krastev (2020) ¹²⁵ (PLB)] - Real-time Detection of Gravitational Waves from Binary Neutron Stars Using Artificial Neural Networks
- [Mytidis et al. (2019) ¹²⁶ (PRD)] - Sensitivity Study Using Machine Learning Algorithms on Simulated $r$ -mode Gravitational Wave Signals from Newborn Neutron Stars
- [Gebhard et al. (2017) ¹²⁷ (Workshop)] - Convwave: Searching for gravitational waves with fully convolutional neural nets
- [Gebhard et al. (2019) ¹²⁸ (PRD)] - Convolutional Neural Networks: A Magic Bullet for Gravitational-wave Detection?
- [Bresten & Jung (2019) ¹²⁹ (1910.08245)] - Detection of Gravitational Waves Using Topological Data Analysis and Convolutional Neural Network: An Improved Approach
- [Santos et al. (2020) ¹³⁰ (2003.09995)] - Gravitational Wave Detection and Information Extraction via Neural Networks
- [Corizzo et al. (2020) ¹³¹ (Expert Syst. Appl.)] - Scalable Auto-Encoders for Gravitational Waves Detection from Time Series Data
- [Chen et al. (2020) ¹³² (Sci. China Phys. Mech. Astron.)] - Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array
- [Marulanda et al. (2020) ¹³³ (PLB)] - Deep learning Gravitational Wave Detection in the Frequency Domain
- [Wang et al. (2020) ¹³⁴ (PRD)] - Gravitational-Wave Signal Recognition of LIGO Data by Deep Learning
- [Kim et al. (2020) ¹³⁵ (PRD)] - Ranking Candidate Signals with Machine Learning in Low-latency Searches for Gravitational Waves from Compact Binary Mergers
- [Schäfer (2019) ¹³⁶ (Masters Thesis)] - Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning
- [Schäfer et al. (2020) ¹³⁷ (PRD)] - Detection of Gravitational-wave Signals from Binary Neutron Star Mergers Using Machine Learning
- [Lin & Wu (2020) ¹³⁸ (PRD)] - Detection of Gravitational Waves Using Bayesian Neural Networks
- [Chauhan (2020) ¹³⁹ (2007.05889)] - Deep Learning Model to Make Gravitational Wave Detections from Weak Time-series Data
- [Singh et al. (2021) ¹⁴⁰ (Mon. Not. Roy. Astron. Soc.)] - Deep Learning for Estimating Parameters of Gravitational Waves
- [Morales et al. (2021) ¹⁴¹ (Sensors)] - Deep Learning for Gravitational-wave Data Analysis: A Resampling White-box Approach
- [Caramete et al. (2020) ¹⁴² (2009.06109)] - Characterization of Gravitational Waves Signals Using Neural Networks
- [Nigam et al. (2020) ¹⁴³ (2009.12168)] - Transient Classification in Low Snr Gravitational Wave Data Using Deep Learning
- [Deighan et al. (2021) [@2021DeighanGeneticalgorithmoptimizedneuralnetworks] (Neural Comput. Appl.)] - Genetic-Algorithm-Optimized Neural Networks for Gravitational Wave Classification
- [Jadhav et al. (2020) ¹⁴⁴ (PRD)] - Improving Significance of Binary Black Hole Mergers in Advanced Ligo Data Using Deep Learning : Confirmation of GW151216
- [Wong et al. (2020) ¹⁴⁵ (2007.10350)] - Gravitational-wave signal-to-noise interpolation via neural networks
- [Wei et al. (2021) ¹⁴⁶ (PLB)] - Deep Learning for Gravitational Wave Forecasting of Neutron Star Mergers
- [Cabero et al. (2020) ¹⁴⁷ (ApJ)] - GWSkyNet: A Real-time Classifier for Public Gravitational-wave Candidates
- [Kim et al. (2020) ¹⁴⁸ (ApJ)] - Identification of Lensed Gravitational Waves with Deep Learning
- [Wei et al. (2021) ¹⁴⁹ (PLB)] - Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
- [Xia et al. (2020) ¹⁵⁰ (PRD)] - Improved Deep Learning Techniques in Gravitational-wave Data Analysis
- [Alvares et al. (2020) ¹⁵¹ (CQG)] - Exploring Gravitational-wave Detection and Parameter Inference Using Deep Learning Methods
- [Wang et al. (2019) ¹²³ (New J. Phys.)] - Identifying Extra High Frequency Gravitational Waves Generated from Oscillons with Cuspy Potentials Using Deep Neural Networks
- LIGO & Virgo provide two probabilities in low-latency. [Chatterjee et al. (2020) [@chatterjee2020machine] (ApJ)] 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.
- [Wei et al. (2020) ¹⁵² (ApJ)] - Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence
- [Menéndez-Vázquez et al. (2020) ¹⁵³ (PRD)] - Searches for Compact Binary Coalescence Events Using Neural Networks in the LIGO/Virgo Second Observation Period
- [Krastev et al. (2020) ¹⁵⁴ (PLB)] - Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data Using Deep Learning
- [Dodia (2021) ¹⁵⁵ (2101.00195)] - Detecting Residues of Cosmic Events Using Residual Neural Network
- [Kulkarni et al. (2019) ¹⁵⁶ (PRD)] - Random Projections in Gravitational Wave Searches of Compact Binaries (Random projections)
- [Rzeza et al. (2021) ¹⁵⁷ (2101.03226)] - Random Projections in Gravitational Wave Searches from Compact Binaries II: Efficient Reconstruction of Detection Statistic within LLOID Framework (Random projections)
- [Zhan et al. (2021) [@2021ZhanResponseConvolutionalNeural] (2103.03557)] - The Response of the Convolutional Neural Network to the Transient Noise in Gravitational Wave Detection
- [Morawski et al. (2021) ¹⁵⁸ (Mach. learn.: sci. technol.)] - Anomaly Detection in Gravitational Waves Data Using Convolutional Autoencoders
- [Baltus et al. (2021) ¹⁵⁹ (PRD)] - Convolutional Neural Networks for the Detection of the Early Inspiral of a Gravitational-wave Signal
- [Yan et al. (2021) ¹⁶⁰ (PRD)] - Generalized Approach to Matched Filtering Using Neural Networks
- [Yu et al. (2021) ¹⁶¹ (PRD)] - Early Warning of Coalescing Neutron-star and Neutron-star-black-hole Binaries from Nonstationary Noise Background Using Neural Networks
- [Fan et al. (2021) ¹⁶² (ICPR)] - Improving Gravitational Wave Detection with 2d Convolutional Neural Networks
- [Baltus et al. (2021) ¹⁶³ (IEEE)] - Detecting the Early Inspiral of a Gravitational-wave Signal with Convolutional Neural Networks
- [Schäfer et al. (2021) ¹⁶⁴ (2106.03741)] - Training Strategies for Deep Learning Gravitational-wave Searches
- [Goyal et al. (2021) ¹⁶⁵ (PRD)] - Rapid Identification of Strongly Lensed Gravitational-wave Events with Machine Learning
- [Dodia et al. (2021) ¹⁶⁶ (2107.03607)] - Specgrav – Detection of Gravitational Waves Using Deep Learning
- [Van Lieshout (2021) ¹⁶⁷ (Master Thesis)] - Sparse, Deep Neural Networks for the Early Detection of Gravitational Waves from Binary Neutron Stars
- [Moreno et al. (2021) ¹⁶⁸ (Mach. learn.: sci. technol.)] - Source-agnostic Gravitational-wave Detection with Recurrent Autoencoders
- [Schäfer et al. (2021) ¹⁶⁹ (PRD)] - From One to Many: A Deep Learning Coincident Gravitational-Wave Search
- [Verma et al. (2021) ¹⁷⁰ (2110.01883)] - Employing Deep Learning for Detection of Gravitational Waves from Compact Binary Coalescences
- [Cuoco et al. (2021) ¹⁷¹ (Universe)] - Multimodal Analysis of Gravitational Wave Signals and Gamma-ray Bursts from Binary Neutron Star Mergers
- [Abbott et al. (2021) ¹⁷² (ApJ)] - GWSkyNet-Multi: A Machine-learning Multiclass Classifier for LIGO-Virgo Public Alerts
- [Ding et al. (2021) ¹⁷³ (2111.09465)] - UniMAP: Model-free Detection of Unclassified Noise Transients in LIGO-Virgo Data Using the Temporal Outlier Factor
- [Moreno et al. (2021) ¹⁶⁸ (2107.12698)] - Source-agnostic Gravitational-wave Detection with Recurrent Autoencoders
- [Ruan et al. (2021) ¹⁷⁴ (PLB)] - Rapid Search for Massive Black Hole Binary Coalescences Using Deep Learning
- [Lopac et al. (2022) ¹⁷⁵ (IEEE Access)] - Detection of Non-stationary GW Signals in High Noise from Cohen’s Class of Time-frequency Representations Using Deep Learning
- [Dahal (2022) ¹⁷⁶ (2201.04086)] - Application of Common Spatial Patterns in Gravitational Waves Detection
- [Chaurvedi et al. (2022) ¹⁷⁷ (Front. Artif. Intell.)] - Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale
- [Zhang et al. (2022) ¹⁷⁸ (PRD)] - Detecting Gravitational-waves from Extreme Mass Ratio Inspirals Using Convolutional Neural Networks
- [Safarzadeh et al. (2022) ¹⁷⁹ (2202.07399)] - Interpreting a Machine Learning Model for Detecting Gravitational Waves
- [Choudhary et al. (2022) ¹⁸⁰ (PRD)] - Deep Learning Network to Distinguish Binary Black Hole Signals from Short-Duration Noise Transients
- [McIsaac & Harry (2022) ¹⁸¹ (2203.03449)] - Using Machine Learning to Auto-Tune Chi-Squared Tests for Gravitational Wave Searches
- [Jiang et al. (2022) ¹⁸² (Front. Phys.)] - Identify Real Gravitational Wave Events in the LIGO-Virgo Catalog GWTC-1 and GWTC-2 with Convolutional Neural Network
- [Ma et al. (2022) [@2022MaEnsembleDeepConvolutional] (PRD)] - Ensemble of Deep Convolutional Neural Networks for Real-Time Gravitational Wave Signal Recognition
- [Baltus et al. (2022) ¹⁸³ (PRD)] - Convolutional Neural Network for Gravitational-Wave Early Alert: Going down in Frequency
- [Kim et al. (2022) ¹⁸⁴ (ApJ)] - Deep Learning-Based Search for Microlensing Signature from Binary Black Hole Events in GWTC-1 and -2
- [Verma et al. (2022) ¹⁸⁵ (2206.12673)] - Can Convolution Neural Networks Be Used for Detection of Gravitational Waves from Precessing Black Hole Systems?
- [Aveiro et al. (2022) ¹⁸⁶ (PRD)] - Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using Object-Detection Machine Learning Models
- [Andrews et al. (2022) ¹⁸⁷ (2207.04749)] - DeepSNR: A Deep Learning Foundation for Offline Gravitational Wave Detection
- [Zhao et al. (2022) ¹⁸⁸ (Communications Physics)] - Space-Based Gravitational Wave Signal Detection and Extraction with Deep Neural Network
- [Jingkai Yan et al. (2022) ¹⁸⁹ (2207.11583)] - Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks
- [Sharma et al. (2022) ¹⁹⁰ (2208.02545)] - Fishing Massive Black Hole Binaries with THAMES
- [Santos et al. (2022) ¹⁹¹ (Expert Syst. Appl.)] - Gravitational Wave Signal Recognition and Ring-down Time Estimation via Artificial Neural Networks
- [Barone et al. (2022) ¹⁹² (2206.06004)] - A Novel Multi-Layer Modular Approach for Real-Time Gravitational-Wave Detection
- [Schäfer et al. (2022) ¹⁹³ (PRD)] - First Machine Learning Gravitational-Wave Search Mock Data Challenge
- [Baltus (2022) ¹⁹⁴ (PhD Thesis)] - A Machine Learning Approach to the Search for Gravitational Waves Emitted by Light Systems
- [Zhang et al. (2022) ¹⁹⁵ (Comput. Intell. Neurosci.)] - Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network
- [Badger et al. (2022) ¹⁹⁶ (PRL)] - Dictionary Learning: A Novel Approach to Detecting Binary Black Holes in the Presence of Galactic Noise with LISA
- [Verma et al. (2022) [@2022VermaEmployingdeeplearning] (AIP Conference Proceedings)] - Employing Deep Learning for Detection of Gravitational Waves from Compact Binary Coalescences
- [Qiu et al. (2022) ¹⁹⁷ (2210.15888)] - Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
- [Nousi et al. (2023) ¹⁹⁸ (PRD)] - Deep Residual Networks for Gravitational Wave Detection
- [Kim (2022) ¹⁹⁹ (2211.02655)] Search for Microlensing Signature in Gravitational Waves from Binary Black Hole Events
- [Yan et al. (2022) ²⁰⁰ (Res. Astron. Astrophys)] - Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR
- [Alhassan et al. (2022) ²⁰¹ (2211.13789)] - Detection of Einstein Telescope Gravitational Wave Signals from Binary Black Holes Using Deep Learning
- [Jiang & Luo (2022) ²⁰² (ICPR)] - Convolutional Transformer for Fast and Accurate Gravitational Wave Detection
- [Andres-Carcasona et al. (2022) [@2022Andres-CarcasonaSearchesMassAsymmetricCompact] (PRD)] - Searches for Mass-Asymmetric Compact Binary Coalescence Events Using Neural Networks in the LIGO/Virgo Third Observation Period
- [Zhang et al. (2022) [@PhysRevD.106.122002] (PRD)] - Deep Learning Model Based on a Bidirectional Gated Recurrent Unit for the Detection of Gravitational Wave Signals
- [Wang et al. (2023) ²⁰³ (2302.00295)] - Self-Supervised Learning for Gravitational Wave Signal Identification
- [Ravichandran et al. (2023) ²⁰⁴ (2302.00666)] - Rapid Identification and Classification of Eccentric Gravitational Wave Inspirals with Machine Learning
- [Shaikh et al. (2022) ²⁰⁵ (IEEE)] - Optimizing Large Gravitational-Wave Classifier through a Custom Cross-System Mirrored Strategy Approach
- [Ma et al. (2023) [@PhysRevD.107.063029] (PRD)] - Artificial Intelligence Model for Gravitational Wave Search Based on the Waveform Envelope
- [Jin et al. (2023) ²⁰⁶ (2305.19003)] - Rapid Identification of Time-Frequency Domain Gravitational Wave Signals from Binary Black Holes Using Deep Learning
- [Jadhav et al. (2023) ²⁰⁷ (2306.11797)] - Towards a Robust and Reliable Deep Learning Approach for Detection of Compact Binary Mergers in Gravitational Wave Data
- [Tian et al. (2023) ²⁰⁸ (2306.15728)] - Physics-Inspired Spatiotemporal-Graph AI Ensemble for Gravitational Wave Detection
- [Pal & Nayak (2023) ²⁰⁹ (2307.03736)] - Swarm-Intelligent Search for Gravitational Waves from Eccentric Binary Mergers
- [Trovato et al. (2023) ²¹⁰ (2307.09268)] - Neural Network Time-Series Classifiers for Gravitational-Wave Searches in Single-Detector Periods
- [Freitas et al. (2023) ²¹¹ (PoS)] - Comparison of Training Methods for Convolutional Neural Network Model for Gravitational-Wave Detection from Neutron Star-Black Hole Binaries

Parameter Estimation (PE)¶

Characterized by 15 parameters. Masses, spins, distance, inclination, sky position, polarization. LIGO & Virgo GWTC-1 [Abbott et al. (2019) ²¹² (PRX)] . Bayes’ Theorem (LAAC tutorial 2019 - Virginia d’Emilio)

MCMC and Nested Sampling
- 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 evidences for model selection. [Skilling (2006) ²¹³ (Bayesian Anal.)]
Machine Learning Parameter Estimation
- The current “holy grail” of machine learning for GWs.
- BAMBI: blind accelerated multimodal Bayesian inference combines the benefits of nested sampling and artificial neural networks. [Graff et al. (2012) ²¹⁴ (Mon. Not. Roy. Astron. Soc.)] An artificial neural network learns the likelihood function to increase significantly the speed of the analysis. [Graff (2012) ²¹⁵ (PhD Thesis)]
- Chua et al. [Chua & Vallisneri (2020) ²¹⁶ (PRL)] produce Bayesian posteriors using neural networks.
- Gabbard et al. [Gabbard et al. (2019) [@2021GabbardBayesianParameterEstimation] (Nature Physics)] 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.
- [Chatterjee et al. (2020) [@chatterjee2020machine] (ApJ)] - A Machine Learning-based Source Property Inference for Compact Binary Mergers
- [Fan et al. (2019) ¹²⁰ (SCI CHINA PHYS MECH)] - Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
- [Green et al. (2020) [@2020GreenGravitationalwaveParameter] (PRD)] - Gravitational-Wave Parameter Estimation with Autoregressive Neural Network Flows
- [Carrillo et al. (2016) ²¹⁷ (GRG)] - Parameter estimates in binary black hole collisions using neural networks
- [Carrillo et al. (2018) ²¹⁸ (INT J MOD PHYS D)] - One parameter binary black hole inverse problem using a sparse training set
- [Chatterjee et al. (2019) ²¹⁹ (PRD)] - Using deep learning to localize gravitational wave sources
- [Yamamoto & Tanaka (2020) ²²⁰ (2002.12095)] - Use of conditional variational auto encoder to analyze ringdown gravitational waves
- [Haegel & Husa (2020) ²²¹ (CQG)] - Predicting the properties of black-hole merger remnants with deep neural networks
- [Belgacem et al. (2020) ²²² (PRD)] - Gaussian processes reconstruction of modified gravitational wave propagation
- [Chen et al. (2020) ¹³² (Sci. China Phys. Mech. Astron.)] - Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array
- [Khan et al. (2020) ²²³ (PLB)] - Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
- [Nakano et al. (2019) ²²⁴ (PRD)] - Comparison of Various Methods to Extract Ringdown Frequency from Gravitational Wave Data
- [Engels et al. (2014) ²²⁵ (PRD)] - Multivariate regression analysis of gravitational waves from rotating core collapse
- [Green & Gair (2021) [@2021GreenCompleteParameterInference] (Mach. learn.: sci. technol.)] - Complete Parameter Inference for GW150914 Using Deep Learning
- [Vivanco et al. (2020) ²²⁶ (Mon. Not. Roy. Astron. Soc.)] - A Scalable Random Forest Regressor for Combining Neutron-Star Equation of State Measurements: A Case Study with GW170817 and GW190425
- [Delaunoy (2020) ²²⁷ (Master Thesis)] - Lightning Gravitational Wave Parameter Inference through Neural Amortization
- [Pacilio (2019) ²²⁸ (Master Thesis)] - Multioutput Regression of Noisy Time Series Using Convolutional Neural Networks with Applications to Gravitational Waves
- [Delaunoy et al. (2020) ²²⁹ (2010.12931)] - Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
- [Marulanda et al. (2020) ¹³³ (PLB)] - Deep Learning Merger Masses Estimation from Gravitational Waves Signals in the Frequency Domain
- [Jeffrey & Wandelt (2020) ²³⁰ (NeurIPS)] - Solving High-dimensional Parameter Inference: Marginal Posterior Densities & Moment Networks
- [Alvares et al. (2020) ¹⁵¹ (CQG)] - Exploring Gravitational-wave Detection and Parameter Inference Using Deep Learning Methods
- [Wang et al. (2019) ¹²³ (New J. Phys.)] - Identifying Extra High Frequency Gravitational Waves Generated from Oscillons with Cuspy Potentials Using Deep Neural Networks
- [Bhagwat & Pacilio (2021) [@2021BhagwatMergerringdownConsistency] (PRD)] - Merger-Ringdown Consistency: A New Test of Strong Gravity Using Deep Learning
- [Williams et al. (2021) ²³¹ (PRD)] - Nested Sampling with Normalising Flows for Gravitational-wave Inference
- [D’Emilio et al. (2021) ²³² (Mon. Not. Roy. Astron. Soc.)] - Density Estimation with Gaussian Processes for Gravitational-wave Posteriors
- [Dax et al. (2021) [@2021DaxRealtimeGravitational] (PRL)] - Real-time Gravitational-wave Science with Neural Posterior Estimation
- [Kuo & Lin (2021) ²³³ (2107.10730)] - Conditional Noise Deep Learning for Parameter Estimation of Gravitational Wave Events
- [Gunny et al. (2021) ²³⁴ (2108.12430)] - Hardware-accelerated Inference for Real-time Gravitational-wave Astronomy
- [Rozet & Louppe (2021) ²³⁵ (2110.00449)] - Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference就是
- [Guedes et al. (2021) ²³⁶ (SBIC)] - Mass Determination of Cosmological Objects from Gravitational Wave Data Using Neural Networks
- [Kolmus et al. (2021) [@2021KolmusSwiftskylocalization] (2111.00833)] - Swift Sky Localization of Gravitational Waves Using Deep Learning Seeded Importance Sampling
- [Dax et al. (2021) [@2021DaxGroupEquivariantNeural] (2111.13139)] - Group Equivariant Neural Posterior Estimation
- [Khan et al. (2021) ²³⁷ (PLB)] - 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
- [Wang et al. (2022) ²³⁸ (BDMA)] - Sampling with Prior Knowledge for High-dimensional Gravitational Wave Data Analysis
- [McLeod et al. (2022) ²³⁹ (2201.11126)] - Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning
- [Sasaoka et al. (2022) ²⁴⁰ (PRD)] - Localization of Gravitational Waves Using Machine Learning
- [Guo et al. (2022) ²⁴¹ (2203.06969)] - Mimicking Mergers: Mistaking Black Hole Captures as Mergers
- [Karamanis et al. (2022) ²⁴² (2207.05652)] - Accelerating Astronomical and Cosmological Inference with Preconditioned Monte Carlo
- [Chatterjee et al. (2022) ²⁴³ (2207.14522)] - Rapid Localization of Gravitational Wave Sources from Compact Binary Coalescences Using Deep Learning
- [Tsatsev (2022) ²⁴⁴ (Masters Thesis)] - Parameter Inference of Gravitational Waves Using Inverse Autoregressive Spline Flow
- [Bayley et al. (2022) ²⁴⁵ (PRD)] - Rapid Parameter Estimation for an All-Sky Continuous Gravitational Wave Search Using Conditional Varitational Auto-Encoders
- [Dax et al. (2022) [@2022DaxNeuralImportanceSampling] (PRL)] - Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
- [Wofford et al. (2022) ²⁴⁶ (PRD)] - Improving Performance for Gravitational-Wave Parameter Inference with an Efficient and Highly-Parallelized Algorithm
- [Wildberger et al. (2022) [@2022WildbergerAdaptingnoisedistribution] (PRD)] - Adapting to Noise Distribution Shifts in Flow-Based Gravitational-Wave Inference
- [Langenorff et al. (2022) ²⁴⁷ (PRL)] - Normalizing Flows as an Avenue to Studying Overlapping Gravitational Wave Signals
- [Chatterjee et al. (2023) ²⁴⁸ (2301.03558)] - Pre-Merger Sky Localization of Gravitational Waves from Binary Neutron Star Mergers Using Deep Learning
- [Wong et al. (2023) ²⁴⁹ (2302.05333)] - Fast Gravitational Wave Parameter Estimation without Compromises
- [Williams et al. (2023) ²⁵⁰ (Mach. learn.: sci. technol.)] - Importance Nested Sampling with Normalising Flows
- [Bhardwaj et al. (2023) [@2023BhardwajPeregrineSequentialsimulationbased] (PRD)] - Sequential simulation-based inference for gravitational wave signals
- [Crisostomi et al. (2023) [@2023CrisostomiNeuralPosteriorEstimationa] (PRD)] - Neural Posterior Estimation with Guaranteed Exact Coverage: The Ringdown of GW150914
- [Dax et al. (2023) [@2023DaxFlowMatchingScalable] (2305.17161)] - Flow Matching for Scalable Simulation-Based Inference
- [Liu et al. (2023) ²⁵¹ (2307.07233)] - Improving the Scalability of Gaussian-process Error Marginalization in Gravitational-Wave Inference
- [Soma et al. (2023) ²⁵² (2306.17488)] - Mass and Tidal Parameter Extraction from Gravitational Waves of Binary Neutron Stars Mergers Using Deep Learning
- [Sun et al. (2023) ²⁵³ (2307.16437)] - Deep Learning Forecasts of Cosmic Acceleration Parameters from DECi-hertz Interferometer Gravitational-wave Observatory
- [Du et al. (2023) ²⁵⁴ (2308.05510)] - Advancing Space-Based Gravitational Wave Astronomy: Rapid Detection and Parameter Estimation Using Normalizing Flows
- [Whittaker et al. (2022) [@PhysRevD.105.124021] (PRD)] - Using Machine Learning to Parametrize Postmerger Signals from Binary Neutron Stars

Population Studies¶

[Vinciguerra et al. (2017) ²⁵⁵ (CQG)] - Enhancing the significance of gravitational wave bursts through signal classification
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 [Abbott et al. (2019) ²⁵⁶ (ApJ)] . 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.
We can use unmodelling clustering! In [Powell et al. (2019) ²⁵⁷ (Mon. Not. Roy. Astron. Soc.)] 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.
(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 code to produce synthetic parameter estimates for compact binaries.
[Varma et al. (2019) ²⁵⁸ (PRL)] - High-Accuracy Mass, Spin, and Recoil Predictions of Generic Black-Hole Merger Remnants
[Deligiannidis et al. (2019) ²⁵⁹ (ICAI)] - Case Study: Skymap Data Analysis
[Wong & Gerosa (2019) ²⁶⁰ (PRD)] - Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: A case study
[Wong et al. (2020) ²⁶¹ (PRD)] - Gravitational-wave population inference with deep flow-based generative network
[Fasano et al. (2020) ²⁶² (PRD)] - Distinguishing Double Neutron Star from Neutron Star-black Hole Binary Populations with Gravitational Wave Observations
[Tiwari (2020) ²⁶³ (CQG)] - VAMANA: Modeling Binary Black Hole Population with Minimal Assumptions
[Vernardos et al. (2020) ²⁶⁴ (Mon. Not. Roy. Astron. Soc.)] - Quantifying the Structure of Strong Gravitational Lens Potentials with Uncertainty-aware Deep Neural Networks
[Wong et al. (2020) ²⁶⁵ (PRD)] - Constraining the Primordial Black Hole Scenario with Bayesian Inference and Machine Learning: The GWTC-2 Gravitational Wave Catalog
[Arjona et al. (2021) ²⁶⁶ (PRD)] - Machine Learning Forecasts of the Cosmic Distance Duality Relation with Strongly Lensed Gravitational Wave Events
[Gerosa et al. (2020) ²⁶⁷ (PRD)] - Gravitational-wave Selection Effects Using Neural-network Classifiers
[Talbot & Thrane (2020) ²⁶⁸ (ApJ)] - Flexible and Accurate Evaluation of Gravitational-wave Malmquist Bias with Machine Learning
[Álvares et al. (2021) ²⁶⁹ (IEEE)] - Gravitational-Wave Parameter Inference Using Deep Learning
[Cheung et al. (2021) ²⁷⁰ (2112.06707)] - Testing the Robustness of Simulation-based Gravitational-wave Population Inference
[Wong et al .(2020) ²⁷¹ (PRD)] - 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
[Mould et al. (2022) ²⁷² (2203.03651)] - Deep Learning and Bayesian Inference of Gravitational-Wave Populations: Hierarchical Black-Hole Mergers
[Strub et al. (2022) ²⁷³ (2204.04467)] - Bayesian Parameter-Estimation of Galactic Binaries in LISA Data with Gaussian Process Regression
[Nakama (2022) ²⁷⁴ (PRD)] - Machine Learning Primordial Black Hole Formation
[Wong et al. (2022) ²⁷⁵ (2207.12409)] - Automated Discovery of Interpretable Gravitational-Wave Population Models
[Mohite (2022) ²⁷⁶ (PhD Thesis)] - Data-Driven Population Inference from Gravitational-Wave Sources and Electromagnetic Counterparts
[Ruhe et al. (2022) ²⁷⁷ (2211.09008)] - Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
[Edelman et al. (2022) ²⁷⁸ (2210.12834)] - Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population
[Ray et al. (2023) ²⁷⁹ (2304.08046)] - Non-Parametric Inference of the Population of Compact Binaries from Gravitational Wave Observations Using Binned Gaussian Processes

5. Continuous Wave Search¶

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
[Morawski et al. (2020) ²⁸⁰ (Mach. learn.: sci. technol.)] - Convolutional Neural Network Classifier for the Output of the Time-domain F-statistic All-sky Search for Continuous Gravitational Waves
[Dreissigacker et al. (2019) ²⁸¹ (PRD)] (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
[Dreissigacker & Prix (2020) ²⁸² (PRD)] - Deep-learning continuous gravitational waves: Multiple detectors and realistic noise
[Bayley et al. (2019) ²⁸³ (PRD)] (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 “raw” 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;
[Miller et al. (2019) ²⁸⁴ (PRD)] - How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
[Miller (2019) ²⁸⁵ (PhD Thesis)] - Using Machine Learning and the Hough Transform to Search for Gravitational Waves Due to R-mode Emission by Isolated Neutron Stars
[Schafer (2019) ¹³⁶ (Masters Thesis)] - Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning
[Beheshtipour & Papa (2020) ²⁸⁶ (PRD)] - Deep Learning for Clustering of Continuous Gravitational Wave Candidates
[Middleton et al. (2020) ²⁸⁷ (PRD)] - Search for Gravitational Waves from Five Low Mass X-ray Binaries in the Second Advanced Ligo Observing Run with an Improved Hidden Markov Model
[Bayley et al. (2020) ²⁸⁸ (PRD)] - Robust Machine Learning Algorithm to Search for Continuous Gravitational Waves
[Bayley (2020) ²⁸⁹ (PhD Thesis)] - Non-parametric and Machine Learning Techniques for Continuous Gravitational Wave Searches
[Jones & Sun (2020) ²⁹⁰ (2007.08732)] - Search for Continuous Gravitational Waves from Fomalhaut B in the Second Advanced Ligo Observing Run with a Hidden Markov Model
[Suvorova et al. (2016) ²⁹¹ (PRD)] - Hidden Markov Model Tracking of Continuous Gravitational Waves from a Neutron Star with Wandering Spin
[Suvorova et al. (2017) ²⁹² (PRD)] - Hidden Markov Model Tracking of Continuous Gravitational Waves from a Binary Neutron Star with Wandering Spin. II. Binary Orbital Phase Tracking
[Sun & Melatos (2019)] ²⁹³ (PRD) - 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
[Sun et al. (2018) ²⁹⁴ (PRD)] - Hidden Markov Model Tracking of Continuous Gravitational Waves from Young Supernova Remnants
[Abbott et al. (2019) ²⁹⁵ (PRD)] - Search for Gravitational Waves from Scorpius X-1 in the Second Advanced LIGO Observing Run with an Improved Hidden Markov Model
[C. Dreißigacker (2020) ²⁹⁶ (PhD Thesis)] - Searches for Continuous Gravitational Waves : Sensitivity Estimation and Deep Learning As a Novel Search Method
[Morawski et al. (2020) ²⁹⁷ (Proceedings)] - achine Learning Classification of Continuous Gravitational-wave Signal Candidates
[Yamamoto & Tanaka (2020) ²⁹⁸ (PRD)] - Use of Excess Power Method and Convolutional Neural Network in All-sky Search for Continuous Gravitational Waves
[Behechtipour & Papa ²⁹⁹ (PRD)] - Deep Learning for Clustering of Continuous Gravitational Wave Candidates II: Identification of low-SNR Candidates
[Beniwal et al. (2021) ³⁰⁰ (PRD)] - Search for Continuous Gravitational Waves from Ten H.E.S.S. Sources Using a Hidden Markov Model
[La Rosa et al. (2021) ³⁰¹ (Universe)] - Continuous Gravitational-Wave Data Analysis with General Purpose Computing on Graphic Processing Units
[Melatos et al. (2021) ³⁰² (PRD)] - Hidden Markov Model Tracking of Continuous Gravitational Waves from a Neutron Star with Wandering Spin. III. Rotational Phase Tracking
[Songsheng et al. (2021) ³⁰³ (ApJ)] - Search for Continuous Gravitational Wave Signals in Pulsar Timing Residuals: A New Scalable Approach with Diffusive Nested Sampling
[Rocha-Solache et al. (2022) [@2022Rocha-SolacheTimedomaindeeplearning] (2201.06672)] - Time-Domain Deep Learning Filtering of Structured Atmospheric Noise for Ground-Based Millimeter Astronomy
[Yamamoto et al. (2022) ³⁰⁴ (PRD)] - Assessing the Impact of Non-Gaussian Noise on Convolutional Neural Networks That Search for Continuous Gravitational Waves
[Vargas & Melatos (2022) ³⁰⁵ (2208.03932)] - Search for Continuous Gravitational Waves from PSR J0437-4715 with a Hidden Markov Model in O3 LIGO Data
[Jochi & Prix (2023) ³⁰⁶ (2305.01057)] - A Novel Neural-Network Architecture for Continuous Gravitational Waves
[Duraisamy et al. (2023) ³⁰⁷ (IEEE)] - Optimized Detection of Continuous Gravitational-Wave Signals Using Convolutional Neural Network
[Pintelas et al. (2023) ³⁰⁸ (Springer Nature Switzerland)] - A Deep Learning-Based Methodology for Detecting and Visualizing Continuous Gravitational Waves
[Dominguez et al. (2023) [@Dominguez:2023W/] (PoS)] - Convolutional Neural Network for Continuous Gravitational Waves Detection

6. Gravitational Wave Bursts¶

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).

Coherent Wave Burst (cWB) Website

cWB 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.
- [Drago et al. (2020) ³⁰⁹ (2006.12604)] - Coherent Waveburst, a Pipeline for Unmodeled Gravitational-wave Data Analysis
- [Mishra et al. (2021) ³¹⁰ (PRD)] - Optimization of Model Independent Gravitational Wave Search Using Machine Learning
- [Mishra et al. (2022) ³¹¹ (2201.01495)] - Search for Binary Black Hole Mergers in the Third Observing Run of Advanced LIGO-Virgo Using Coherent Waveburst Enhanced with Machine Learning
- [Szczepańczyk et al. (2023) [@PhysRevD.107.062002] (PRD)] - Search for Gravitational-Wave Bursts in the Third Advanced LIGO-Virgo Run with Coherent WaveBurst Enhanced by Machine Learning
BayesWave

BayesWave is another standard burst tool. [Cornish & Littenberg (2015) ³¹² (CQG)] 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. [Pankow et al. (2018) ³¹³ (PRD)]
Supernova Search

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.
- [Astone et al. (2018) ⁴² (PRD)] 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.
- [Iess et al. (2020) ³¹⁴ (Mach. learn.: sci. technol.)] 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.
- [Chan et al. (2019) ³¹⁵ (PRD)] also train directly on supernova waveforms. They use only the time series waveforms from different explosion mechanisms.
- [Cavaglia et al. (2020) [@Cavaglia2020qzp] (Mach. learn.: sci. technol.)] - Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach
- [Stachie et al. (2020) ³¹⁶ (Mon. Not. Roy. Astron. Soc.)] - Using Machine Learning for Transient Classification in Searches for Gravitational-wave Counterparts
- [Marianer et al. (2020) ⁴⁹ (Mon. Not. Roy. Astron. Soc.)] - A Semisupervised Machine Learning Search for Never-Seen Gravitational-Wave Sources
- [Millhouse et al. (2020) ³¹⁷ (PRD)] - Search for Gravitational Waves from 12 Young Supernova Remnants with a Hidden Markov Model in Advanced LIGO’s Second Observing Run
- [L'opez et al. (2021) ³¹⁸ (PRD)] - Deep Learning for Core-collapse Supernova Detection
- [L'operz et al. (2021) ³¹⁹ (IEEE)] - Deep Learning Algorithms for Gravitational Waves Core-collapse Supernova Detection
- [Antelis et al. (2021) ³²⁰ (PRD)] - Using Supervised Learning Algorithms As a Follow-up Method in the Search of Gravitational Waves from Core-collapse Supernovae
- [Lagos et al. (2023) ³²¹ (2304.11498)] - Characterizing the Gravitational Wave Temporal Evolution of the Gmode Fundamental Resonant Frequency for a Core Collapse Supernova: A Neural Network Approach
Burst analysis

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.

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
- [Rover et al. (2009) ³²² (PRD)] - Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce
- [Kovačević et al. (2019) [#Kovacevic2019wpy] (Mon. Not. Roy. Astron. Soc.)] - Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources
- [Kim et al. (2015) ³²³ (CQG)] - Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
- [Gayathri et al. (2020) ³²⁴ (PRD)] - Enhancing the Sensitivity of Transient Gravitational Wave Searches with Gaussian Mixture Models
- [L'opez et al. (2021) ³¹⁸ (PRD)] - Deep Learning for Core-collapse Supernova Detection
- [Skliris et al. (2020) [@2020SklirisRealtimeDetection] (2009.14611)] - Real-time Detection of Unmodeled Gravitational-wave Transients Using Convolutional Neural Networks
- [Lopez et al. (2021) ³²⁵ (PRD)] - Utilizing Gaussian Mixture Models in All-Sky Searches for Short-Duration Gravitational Wave Bursts
- [Boudart & Fays (2022) ³²⁶ (PRD)] - A Machine Learning Algorithm for Minute-Long Burst Searches
- [Mitra et al. (2022) ³²⁷ (Mon. Not. Roy. Astron. Soc.)] - Exploring Supernova Gravitational Waves with Machine Learning
- [Szczepańczyk et al. (2022) ³²⁸ (2210.01754)] - All-Sky Search for Gravitational-Wave Bursts in the Third Advanced LIGO-Virgo Run with Coherent WaveBurst Enhanced by Machine Learning
- [Iess et al. (2022) ³²⁹ (A&A)] - LSTM and CNN Application for Core-Collapse Supernova Search in Gravitational Wave Real Data
- [Boudart & Fays (2022) ³³⁰ (IEEE)] - ALBUS: A Machine Learning Algorithm for Gravitational Wave Burst Searches
- [Modafferi et al. (2022) ³³¹ (2303.16720)] - Convolutional Neural Network Search for Long-Duration Transient Gravitational Waves from Glitching Pulsars
- [Sasaoka et al. (2023) [@Sasaoka:20232F] (PoS)] - Deep Learning for Detecting Gravitational Waves from Compact Binary Coalescences and Its Visualization by Grad-CAM
- [Meijer et al. (2023) ³³² (2308.12323)] - Gravitational-Wave Searches for Cosmic String Cusps in Einstein Telescope Data Using Deep Learning

Single Detector Search

30% of gravitational wave data is collected when only 1 detector is in observing mode. Can’t do time slides to measure the background if there is only 1 detector.
- [Cavaglia et al. (2020) [@Cavaglia2020qzp] (Mach. learn.: sci. technol.)] 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.
- (How can I try it myself?) You can download some supernova gravitational wave signals here . You can get KarooGP here . You can get Coherent WaveBurst here.

7. Stochastic Gravitational Wave Background¶

[Utina et al. (2021) ³³³ (IEEE)] - Deep Learning Searches for Gravitational Wave Stochastic Backgrounds
[Yamamoto et al. (2022) ³³⁴ (PRD)] - Deep Learning for Intermittent Gravitational Wave Signals

8. GW / Cosmology¶

[Khan et al. (2019) ³³⁵ (PLB)] 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;
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 [Wei et al. (2020) ³³⁶ (Mon. Not. Roy. Astron. Soc.)]. 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.
[Alexander et al. (2020) ³³⁷ (ApJ)] - Deep Learning the Morphology of Dark Matter Substructure
[Gupta & Reichardt (2020) ³³⁸ (ApJ)] - Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel’dovich Effect
[Sadr & Farsian (2020) ³³⁹ (JCAP)] - Inpainting via Generative Adversarial Networks for CMB data analysis
[Philip et al. (2002) ³⁴⁰ (Astron. Astrophys.)] - A difference boosting neural network for automated star-galaxy classification
[Philip et al. (2012) ³⁴¹ (1211.3607)] - Classification by Boosting Differences in Input Vectors: An application to datasets from Astronomy
[Sadeh (2020) ³⁴² (ApJ)] - Data-driven Detection of Multimessenger Transients
[Wang et al (2020) ³⁴³ (ApJS)] - ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks
- 他们这个是当做回归问题在做，和 Huerta 他们的基本逻辑其实一样。至于6 个目标参数的估计，是通过给定 input 数据在相应参数区间上采样后，直接给出后验样本参数估计的（频率学派~）。并不是对某单个数据给出的参数估计（贝叶斯学派）。
- 实现的是对应观测数据集的宇宙学参数估计
[Xu et al. (2020) ³⁴⁴ (PASP)] - GWOPS: A Vo-technology Driven Tool to Search for the Electromagnetic Counterpart of Gravitational Wave Event
[Milosevic et al. (2020) ³⁴⁵ (A&A)] - Bayesian Decomposition of the Galactic Multi-frequency Sky Using Probabilistic Autoencoders
[Hortua et al. (2019) ³⁴⁶ (PRD)] - Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks
[Matilla et al. (2020) ³⁴⁷ (PRD)] - Interpreting Deep Learning Models for Weak Lensing
[Guzman & Meyers (2021) ³⁴⁸ (PRD))] - Reconstructing Patchy Reionization with Deep Learning
[Boilla et al. (2021) ³⁴⁹ (2102.06149)] - Reconstruction of the Dark Sectors’ Interaction: A Model-independent Inference and Forecast from GW Standard Sirens
[Ren et al. (2021) ³⁵⁰ (2103.01260)] - Data-driven Reconstruction of the Late-time Cosmic Acceleration with F(t) Gravity
[Yang (2021) ³⁵¹ (2103.01923)] - Gravitational-wave Detector Networks: Standard Sirens on Cosmology and Modified Gravity Theory
[Han et al. (2021) ³⁵² (ApJ)] - Bayesian Nonparametric Inference of Neutron Star Equation of State Via Neural Network
[Edwards (2020) ³⁵³ (PRD)] - Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
[Natarajan et al. (2021) ³⁵⁴ (2103.13932)] - Quasarnet: A New Research Platform for the Data-driven Investigation of Black Holes
[Elizalde et al. (2021) ³⁵⁵ (2104.01077)] - An Approach to Cold Dark Matter Deviation and the $H_{0}$ Tension Problem by Using Machine Learning
[Gómez-Vargas et al. (2021) ³⁵⁶ (2104.00595)] - Cosmological Reconstructions with Artificial Neural Networks
[Tilaver et al. (2021) ³⁵⁷ (Comput. Phys. Commun)] - Deep Learning Approach to Hubble Parameter
[Gerardi et al. (2021) ³⁵⁸ (PRD)] - Unbiased Likelihood-free Inference of the Hubble Constant from Light Standard Sirens
[Velasquez-Toribio et al. (2021) ³⁵⁹ (2104.07356)] - Constraints on Cosmographic Functions Using Gaussian Processes
[Cañas-Herrera et al. (2021) ³⁶⁰ (ApJ)] - Learning How to Surf: Reconstructing the Propagation and Origin of Gravitational Waves with Gaussian Processes
[Rouhiainen et al. (2021) ³⁶¹ (2105.12024)] - Normalizing Flows for Random Fields in Cosmology
[Mancarella et al. (2022) ³⁶² (PRD)] - Seeking New Physics in Cosmology with Bayesian Neural Networks: Dark Energy and Modified Gravity
[Chapman-Bird et al. (2022) [@2022Chapman-BirdRapiddeterminationLISA] (2212.06166)] - Rapid Determination of LISA Sensitivity to Extreme Mass Ratio Inspirals with Machine Learning
[Gagnon-Hartman et al. (2023) [@2023Gagnon-HartmanDebiasingStandardSiren] (2301.05241)] Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation
[Shah et al. (2023) ³⁶³ (2301.12708)] - A Thorough Investigation of the Prospects of eLISA in Addressing the Hubble Tension: Fisher Forecast, MCMC and {{Machine Learning
[Ashton (2023) ³⁶⁴ (Mon. Not. Roy. Astron. Soc.)] - Gaussian Processes for Glitch-robust Gravitational-wave Astronomy
[Callister & Farr (2023) ³⁶⁵ (2302.07289)] - A Parameter-Free Tour of the Binary Black Hole Population
[Riley & Mandel (2023) ³⁶⁶ (2303.00508)] - Probing Cosmic History with Merging Compact Binaries
[Mukherjee et al. (2023) ³⁶⁷ (2303.05169)] - Reconstructing the Hubble Parameter with Future Gravitational Wave Missions Using Machine Learning
[Alvey et al. (2023) ³⁶⁸ (2304.02032)] - Albatross: A Scalable Simulation-Based Inference Pipeline for Analysing Stellar Streams in the Milky Way
[Whittle et al. (2023) ³⁶⁹ (2305.13780)] - Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
[Sravan et al. (2023) ³⁷⁰ (2307.09213)] - Machine-Directed Gravitational-Wave Counterpart Discovery

9. Physics related¶

Some selected interesting works:

[Funai et al. (2018) ³⁷¹ (PRR)] - Thermodynamics and Feature Extraction by Machine Learning
[Breen et al. (2019) ³⁷² (Mon. Not. Roy. Astron. Soc.)] - Newton Vs the Machine: Solving the Chaotic Three-body Problem Using Deep Neural Networks | 深度学习求解「三体」问题，计算速度提高一亿倍 | 牛顿解决不了的问题，AI或许能搞定：用神经网络解决三体问题
[Greydanus et al. (2019) ³⁷³ (NeurIPS)] - Hamiltonian Neural Networks
[Cohen et al. (2019) ³⁷⁴（PRR)] - Learning Curves for Overparametrized Deep Neural Networks: A Field Theory Perspective
[Rosofsky & Huerta (2020) ³⁷⁵ (PRD)] - Artificial Neural Network Subgrid Models of 2D Compressible Magnetohydrodynamic Turbulence
[Tamayo et al. (2020) ³⁷⁶ (PNAS)] - Predicting the Long-term Stability of Compact Multiplanet Systems

Sagan学者Dan Tamayo介绍了他们在PNAS上发表的一篇利用机器学习技术预测多行星系统的动力学稳定性。(Informative comments from 光头怪博士)
[Green & Ting (2020) ³⁷⁷ (2011.04673)] - Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space
[Liu & Tegmark (2021) ³⁷⁸ (PRL)] - Machine Learning Conservation Laws from Trajectories
[Lucie-Smith et al. (2020) ³⁷⁹ (2011.10577)] - Deep Learning Insights into Cosmological Structure Formation
[Yip et al. (2020) ³⁸⁰ (ApJ)] - Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals
[Glüsenkamp (2020)³⁸¹ (2008.05825)] - Unifying Supervised Learning and VAEs – Automating Statistical Inference in High-energy Physics
[Rousseau et al. (2020) ³⁸² (AIHEP)] - Machine Learning Scientific Competitions and Datasets
[Cranmer et al. (2021) ³⁸³ (PNAS)] - A Bayesian Neural Network Predicts the Dissolution of Compact Planetary Systems
[Kochkov et al. (2021) ³⁸⁴ (PANS)] - Machine Learning Accelerated Computational Fluid Dynamics

机器学习与流体动力学：谷歌AI利用「ML+TPU」实现流体模拟数量级加速
[Visschers et al. (2021) ³⁸⁵ (Mach. learn.: sci. technol.)] - Rapid Parameter Estimation of Discrete Decaying Signals Using Autoencoder Networks
[Guidetti et al. (2021) ³⁸⁶ (2103.08662)] - dNNsolve: an efficient NN-based PDE solver
[Liu et al. (2021) ³⁸⁷ (PRE)] - Machine-learning Non-conservative Dynamics for New-physics Detection
[Rosofsky & Huerta (2022) ³⁸⁸ (Mach. learn.: sci. technol.)] - Applications of Physics Informed Neural Operators
[Katsube et al. (2022) ³⁸⁹ (PRD)] - Deep Learning Metric Detectors in General Relativity
[Luna et al. (2022) ³⁹⁰ (2212.06103)] - Solving the Teukolsky Equation with Physics-Informed Neural Networks
[Cornell et al. (2022) [@PhysRevD.106.124047] （PRD）] - Using Physics-Informed Neural Networks to Compute Quasinormal Modes
[Hatefi et al. (2023) ³⁹¹ (2302.04619)] - Analysis of Black Hole Solutions in Parabolic Class Using Neural Networks
[Sabbatini & Grimani (2023) ³⁹² (2302.06740)] - Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA
[Ma & Vajente (2023) ³⁹³ (2302.07921)] - A Deep Learning Technique to Control the Non-linear Dynamics of a Gravitational-wave Interferometer
[Rosofsky & Huerta (2023) ³⁹⁴ (2302.08332)] - Magnetohydrodynamics with Physics Informed Neural Operators
[Lim et al. (2023) ³⁹⁵ (2305.13358)] - Mapping Dark Matter in the Milky Way Using Normalizing Flows and Gaia DR3
[Dialektopoulos et al. (2023) ³⁹⁶ (2305.15500)] - Neural Network Reconstruction of Scalar-Tensor Cosmology
[Liu & Max (2022) ³⁹⁷ (PRL)] - Machine-Learning Hidden Symmetries

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