Applications of machine learning in gravitational wave research with current interferometric detectors

Abstract

This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days, but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning data after it is taken have already become crucial tools for achieving the best sensitivity of the LIGO--Virgo--KAGRA network. In data analysis, machine learning has already been applied as an alternative to traditional methods for signal detection, source localization, noise reduction, and parameter estimation. For some signal types, it can already yield improved efficiency and robustness, though in many other areas traditional methods remain dominant. As the field evolves, the role of machine learning in advancing gravitational-wave research is expected to become increasingly prominent. This report highlights recent advancements, challenges, and perspectives for the current detector generation, with a brief outlook to the next generation of gravitational-wave detectors.

References in corpus (310)

• XGBoost: A Scalable Tree Boosting System
• Observation of Gravitational Waves from a Binary Black Hole Merger
• emcee: The MCMC Hammer
• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Conditional Generative Adversarial Nets
• A Brief Survey of Deep Reinforcement Learning
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Multi-messenger Observations of a Binary Neutron Star Merger
• Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• Advanced LIGO
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
• Interpretable machine learning: definitions, methods, and applications
• TianQin: a space-borne gravitational wave detector
• Densely Connected Convolutional Networks
• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• GW190425: Observation of a Compact Binary Coalescence with Total Mass $\sim 3.4M_{\odot }$
• Properties of the binary neutron star merger GW170817
• Exploring the Sensitivity of Next Generation Gravitational Wave Detectors
• Did LIGO detect dark matter?
• Science Case for the Einstein Telescope
• Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
• Frequency-domain gravitational waves from non-precessing black-hole binaries. II. A phenomenological model for the advanced detector era
• Gravitational Wave Experiments and Early Universe Cosmology
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
• Frequency-domain gravitational waves from non-precessing black-hole binaries. I. New numerical waveforms and anatomy of the signal
• Observation of gravitational waves from two neutron star-black hole coalescences
• Detecting a stochastic background of gravitational radiation: Signal processing strategies and sensitivities
• Black holes, gravitational waves and fundamental physics: a roadmap
• Data analysis of gravitational-wave signals from spinning neutron stars. I. The signal and its detection
• The PyCBC search for gravitational waves from compact binary coalescence
• FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries
• An improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors
• Taiji Program: Gravitational-Wave Sources
• Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo
• Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue
• Self-force and radiation reaction in general relativity
• Mask R-CNN
• Surrogate models for precessing binary black hole simulations with unequal masses
• A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals
• The TianQin project: current progress on science and technology
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914
• New binary black hole mergers in the second observing run of Advanced LIGO and Advanced Virgo
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
• Multipolar Effective-One-Body Waveforms for Precessing Binary Black Holes: Construction and Validation
• Dynamic temperature selection for parallel-tempering in Markov chain Monte Carlo simulations
• Time-domain effective-one-body gravitational waveforms for coalescing compact binaries with nonprecessing spins, tides and self-spin effects
• Coherent method for detection of gravitational wave bursts
• Setting the cornerstone for the IMRPhenomX family of models for gravitational waves from compact binaries: The dominant harmonic for non-precessing quasi-circular black holes
• Surrogate model of hybridized numerical relativity binary black hole waveforms
• Real-time gravitational-wave science with neural posterior estimation
• Matching matched filtering with deep networks in gravitational-wave astronomy
• 2-OGC: Open Gravitational-wave Catalog of binary mergers from analysis of public Advanced LIGO and Virgo data
• Constraints on the cosmic expansion history from GWTC-3
• Multiresolution techniques for the detection of gravitational-wave bursts
• LIGO Detector Characterization in the Second and Third Observing Runs
• Spin orientations of merging black holes formed from the evolution of stellar binaries
• Relativistic Dynamics and Extreme Mass Ratio Inspirals
• Fast prediction and evaluation of gravitational waveforms using surrogate models
• A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• 1-OGC: The first open gravitational-wave catalog of binary mergers from analysis of public Advanced LIGO data
• Frequency domain reduced order models for gravitational waves from aligned-spin compact binaries
• Nonparametric Inference of Neutron Star Composition, Equation of State, and Maximum Mass with GW170817
• Hierarchical data-driven approach to fitting numerical relativity data for nonprecessing binary black holes with an application to final spin and radiated energy
• Constraints on cosmic strings using data from the third Advanced LIGO-Virgo observing run
• Frequency domain reduced order model of aligned-spin effective-one-body waveforms with generic mass-ratios and spins
• Enhancing Gravitational-Wave Science with Machine Learning
• Multi-Messenger Astrophysics
• Massive Primordial Black Holes as Dark Matter and their detection with Gravitational Waves
• Gravitational-wave parameter estimation with autoregressive neural network flows
• SEOBNRv5PHM: Next generation of accurate and efficient multipolar precessing-spin effective-one-body waveforms for binary black holes
• Laying the foundation of the effective-one-body waveform models SEOBNRv5: improved accuracy and efficiency for spinning non-precessing binary black holes
• All-sky search for gravitational-wave bursts in the second joint LIGO-Virgo run
• A Mock Data Challenge for the Einstein Gravitational-Wave Telescope
• Nested sampling for physical scientists
• PyCBC Live: Rapid Detection of Gravitational Waves from Compact Binary Mergers
• A Surrogate Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers
• The MBTA Pipeline for Detecting Compact Binary Coalescences in the Third LIGO-Virgo Observing Run
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• LISA Data Analysis using MCMC methods
• Identification and mitigation of narrow spectral artifacts that degrade searches for persistent gravitational waves in the first two observing runs of Advanced LIGO
• Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation
• High-accuracy mass, spin, and recoil predictions of generic black-hole merger remnants
• Nested Sampling with Normalising Flows for Gravitational-Wave Inference
• Machine Learning in Astronomy: a practical overview
• Searching for Dark Photon Dark Matter with Gravitational Wave Detectors
• Search for gravitational waves from a long-lived remnant of the binary neutron star merger GW170817
• Effects of Data Quality Vetoes on a Search for Compact Binary Coalescences in Advanced LIGO's First Observing Run
• Machine-learning non-stationary noise out of gravitational wave detectors
• Searches for Continuous-Wave Gravitational Radiation
• Localization accuracy of compact binary coalescences detected by the third-generation gravitational-wave detectors and implication for cosmology
• Novel signatures of dark matter in laser-interferometric gravitational-wave detectors
• An Introduction to Autoencoders
• Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
• Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
• Interpreting Binary Neutron Star Mergers: Describing the Binary Neutron Star Dynamics, Modelling Gravitational Waveforms, and Analyzing Detections
• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• GLADE+: An Extended Galaxy Catalogue for Multimessenger Searches with Advanced Gravitational-wave Detectors
• The evolution of binary neutron star post-merger remnants: a review
• Coherent WaveBurst, a pipeline for unmodeled gravitational-wave data analysis
• Direct limits for scalar field dark matter from a gravitational-wave detector
• Distinguishing short duration noise transients in LIGO data to improve the PyCBC search for gravitational waves from high mass binary black hole mergers
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• All-sky search for gravitational wave emission from scalar boson clouds around spinning black holes in LIGO O3 data
• Hidden Markov model tracking of continuous gravitational waves from a neutron star with wandering spin
• Reconstructing the calibrated strain signal in the Advanced LIGO detectors
• Long gravitational-wave transients and associated detection strategies for a network of terrestrial interferometers
• Deep-Learning continuous gravitational waves
• Classification methods for noise transients in advanced gravitational-wave detectors
• Noise Reduction in Gravitational-wave Data via Deep Learning
• Constraints on dark photon dark matter using data from LIGO's and Virgo's third observing run
• Search method for long-duration gravitational-wave transients from neutron stars
• All-sky search for short gravitational-wave bursts in the third Advanced LIGO and Advanced Virgo run
• Bilby-MCMC: An MCMC sampler for gravitational-wave inference
• The optimal search for an astrophysical gravitational-wave background
• Fast and Accurate Sensitivity Estimation for Continuous-Gravitational-Wave Searches
• An early warning system for electromagnetic follow-up of gravitational-wave events
• Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
• Numerical Relativity of Compact Binaries in the 21st Century
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• Search for continuous gravitational waves: Improving robustness versus instrumental artifacts
• Searches for continuous gravitational waves from neutron stars: A twenty-year retrospective
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• Environmental Noise in Advanced LIGO Detectors
• Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy
• How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
• Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
• Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
• The cross-correlation search for periodic gravitational waves
• Methodology study of machine learning for the neutron star equation of state
• Search for subsolar-mass binaries in the first half of Advanced LIGO and Virgo's third observing run
• Searching for Dark Photon Dark Matter in LIGO O1 Data
• Finding the origin of noise transients in LIGO data with machine learning
• Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• First demonstration of early warning gravitational wave alerts
• Statistical Gravitational Waveform Models: What to Simulate Next?
• Search methods for continuous gravitational-wave signals from unknown sources in the advanced-detector era
• Deep learning for multimessenger core-collapse supernova detection
• Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
• Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
• Virgo Detector Characterization and Data Quality: results from the O3 run
• Machine Learning Gravitational Waves from Binary Black Hole Mergers
• Efficient Gravitational-wave Glitch Identification from Environmental Data Through Machine Learning
• Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications
• Calibration of Advanced Virgo and Reconstruction of the Gravitational Wave Signal h(t) during the Observing Run O2
• On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors
• Deep learning for clustering of continuous gravitational wave candidates
• Status and perspectives of Continuous Gravitational Wave searches
• SuperRad: Modeling the black hole superradiance gravitational waveform
• Non-parametric inference of the population of compact binaries from gravitational wave observations using binned Gaussian processes
• Improving gravitational-wave parameter estimation using Gaussian process regression
• Denoising of gravitational wave signals via dictionary learning algorithms
• Methods for Reducing False Alarms in Searches for Compact Binary Coalescences in LIGO Data
• Hidden Markov model tracking of continuous gravitational waves from a binary neutron star with wandering spin. II. Binary orbital phase tracking
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• A Precessing Numerical Relativity Waveform Surrogate Model for Binary Black Holes: A Gaussian Process Regression Approach
• Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise
• Time-Frequency Analysis of Gravitational Wave Data
• Hidden Markov model tracking of continuous gravitational waves from young supernova remnants
• A Machine Learning Based Source Property Inference for Compact Binary Mergers
• Applications of physics informed neural operators
• Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
• A scalable random forest regressor for combining neutron-star equation of state measurements: A case study with GW170817 and GW190425
• 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
• Importance nested sampling with normalising flows
• Training Strategies for Deep Learning Gravitational-Wave Searches
• Searching for gravitational-wave transients with a qualitative signal model: seedless clustering strategies
• Novel Method for Incorporating Model Uncertainties into Gravitational Wave Parameter Estimates
• Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
• Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning
• Bayesian nonparametric inference of neutron star equation of state via neural network
• Deep Einstein@Home all-sky search for continuous gravitational waves in LIGO O3 public data
• Constraints on planetary and asteroid-mass primordial black holes from continuous gravitational-wave searches
• Application of dictionary learning to denoise LIGO's blip noise transients
• Hierarchical multi-stage MCMC follow-up of continuous gravitational wave candidates
• Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal
• Deep Residual Networks for Gravitational Wave Detection
• The physics of Core-Collapse Supernovae: explosion mechanism and explosive nucleosynthesis
• Gravitational wave population inference with deep flow-based generative network
• Conditional Variational Autoencoder for Neural Machine Translation
• The most powerful astrophysical events: Gravitational-wave peak luminosity of binary black holes as predicted by numerical relativity
• Dynamic Normalization for Compact Binary Coalescence Searches in Non-Stationary Noise
• Computing Fast and Reliable Gravitational Waveforms of Binary Neutron Star Merger Remnants
• Impact of noise transients on low latency gravitational-wave event localisation
• Gravitational-wave Merger Forecasting: Scenarios for the early detection and localization of compact-binary mergers with ground based observatories
• Machine learning for gravitational-wave detection: surrogate Wiener filtering for the prediction and optimized cancellation of Newtonian noise at Virgo
• Improving the background of gravitational-wave searches for core collapse supernovae: A machine learning approach
• Early warning of coalescing neutron-star and neutron-star-black-hole binaries from nonstationary noise background using neural networks
• Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates
• Stochastic gravitational wave background: methods and Implications
• Space-based gravitational wave signal detection and extraction with deep neural network
• Hierarchical follow-up of sub-threshold candidates of an all-sky Einstein@Home search for continuous gravitational waves on LIGO sixth science run data
• Gravitational-wave selection effects using neural-network classifiers
• SOAP: A generalised application of the Viterbi algorithm to searches for continuous gravitational-wave signals
• A LISA Data-Analysis Primer
• First search for ultralight dark matter with a space-based gravitational-wave antenna: LISA Pathfinder
• Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks
• Regression methods in waveform modeling: a comparative study
• Optimization of model independent gravitational wave search using machine learning
• Solving the Teukolsky equation with physics-informed neural networks
• Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis And Convolutional Neural Networks
• Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run
• Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study
• Gravitational wave populations and cosmology with neural posterior estimation
• Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by Machine Learning
• A new veto for continuous gravitational wave searches
• Adaptive clustering procedure for continuous gravitational wave searches
• Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216
• Gravity Spy: Lessons Learned and a Path Forward
• An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
• GWSkyNet: a real-time classifier for public gravitational-wave candidates
• A robust machine learning algorithm to search for continuous gravitational waves
• Limiting the effects of earthquakes on gravitational-wave interferometers
• Hidden Markov model tracking of continuous gravitational waves from a neutron star with wandering spin. III. Rotational phase tracking
• Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates
• Application of a hierarchical MCMC follow-up to Advanced LIGO continuous gravitational-wave candidates
• Early Warnings of Binary Neutron Star Coalescence using the SPIIR Search
• PyFstat: a Python package for continuous gravitational-wave data analysis
• Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
• Identification of Lensed Gravitational Waves with Deep Learning
• Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts
• Search strategies for long gravitational-wave transients: hidden Markov model tracking and seedless clustering
• Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with machine learning
• A semi-supervised Machine Learning search for never-seen Gravitational-Wave sources
• Generating transient noise artifacts in gravitational-wave detector data with generative adversarial networks
• Gravitational wave constraints on planetary-mass primordial black holes using LIGO O3a data
• Magnetohydrodynamics with Physics Informed Neural Operators
• Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
• Classification of the core-collapse supernova explosion mechanism with learned dictionaries
• Rapid localization of gravitational wave sources from compact binary coalescences using deep learning
• Using machine learning to parametrize postmerger signals from binary neutron stars
• GWitchHunters: Machine Learning and citizen science to improve the performance of Gravitational Wave detector
• Introduction to Numerical Relativity
• Does non-stationary noise in LIGO and Virgo affect the estimation of $H_0$?
• Use of Excess Power Method and Convolutional Neural Network in All-Sky Search for Continuous Gravitational Waves
• Generalised gravitational burst generation with Generative Adversarial Networks
• Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders
• Faster search for long gravitational-wave transients: GPU implementation of the transient F-statistic
• A classifier for gravitational-wave inspiral signals in non-ideal single-detector data
• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
• Predicting the properties of black holes merger remnants with Deep Neural Networks
• Virgo Detector Characterization and Data Quality: tools
• Optimising the choice of analysis method for all-sky searches for continuous gravitational waves with Einstein@Home
• Deep Learning Model on Gravitational Waveforms in Merging and Ringdown Phases of Binary Black Hole Coalescences
• Gravitational-Wave Searches for Cosmic String Cusps in Einstein Telescope Data using Deep Learning
• Using physics-informed neural networks to compute quasinormal modes
• Modelling Force-Free Neutron Star Magnetospheres using Physics-Informed Neural Networks
• Results from an Einstein@Home search for continuous gravitational waves from G347.3 at low frequencies in LIGO O2 data
• Enhancing the sensitivity of transient gravitational wave searches with Gaussian Mixture Models
• Convolutional neural network search for long-duration transient gravitational waves from glitching pulsars
• Probabilistic Model for the Gravitational Wave Signal from Merging Black Holes
• Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics
• Sensitivity study using machine learning algorithms on simulated r-mode gravitational wave signals from newborn neutron stars
• Searches for Mass-Asymmetric Compact Binary Coalescence Events using Neural Networks in the LIGO/Virgo Third Observation Period
• SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
• Learning orbital dynamics of binary black hole systems from gravitational wave measurements
• Applying the Viterbi Algorithm to Planetary-Mass Black Hole Searches
• GWSkyNet-Multi: A Machine Learning Multi-Class Classifier for LIGO-Virgo Public Alerts
• Detection and Mitigation of Glitches in LISA Data: A Machine Learning Approach
• Nonparametric Representation of Neutron Star Equation of State Using Variational Autoencoder
• Premerger detection of massive black hole binaries using deep learning
• SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
• Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling
• Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
• Tracking continuous gravitational waves from a neutron star at once and twice the spin frequency with a hidden Markov model
• New methods to assess and improve LIGO detector duty cycle
• Multi-messenger observations of core-collapse supernovae: Exploiting the standing accretion shock instability
• Solving the Pulsar Equation using Physics-Informed Neural Networks
• Gravitational wave peak luminosity model for precessing binary black holes
• Time-frequency track distance for comparing continuous gravitational wave signals
• Enhancing the capabilities of LIGO time-frequency plane searches through clustering
• Visualizing convolutional neural network for classifying gravitational waves from core-collapse supernovae
• Detection of Einstein Telescope gravitational wave signals from binary black holes using deep learning
• Validating continuous gravitational-wave candidates from a semicoherent search using Doppler modulation and an effective point spread function
• Exploring a search for long-duration transient gravitational waves associated with magnetar bursts
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Neural network time-series classifiers for gravitational-wave searches in single-detector periods
• Understanding of the properties of neural network approaches for transient light curve approximations
• Density-clustering of continuous gravitational wave candidates from large surveys
• Assessing the impact of non-Gaussian noise on convolutional neural networks that search for continuous gravitational waves
• Deep learning for intermittent gravitational wave signals
• Bayesian $\mathcal{F}$-statistic-based parameter estimation of continuous gravitational waves from known pulsars
• A convolutional neural network to distinguish glitches from minute-long gravitational wave transients
• Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning
• Testing the robustness of simulation-based gravitational-wave population inference
• Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations
• Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network
• Is machine learning good or bad for the natural sciences?
• Convolutional Neural Networks for signal detection in real LIGO data
• Piecewise frequency model for searches for long-transient gravitational waves from young neutron stars
• Novel neural-network architecture for continuous gravitational waves
• Improving the scalability of Gaussian-process error marginalization in gravitational-wave inference
• Optimizing the Placement of Numerical Relativity Simulations using a Mismatch Predicting Neural Network
• Toward a computationally-efficient follow-up pipeline for blind continuous gravitational-wave searches
• Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
• Gravitational Waves from Neutrino-Driven Core Collapse Supernovae: Predictions, Detection, and Parameter Estimation
• Large-kernel Convolutional Neural Networks for Wide Parameter-Space Searches of Continuous Gravitational Waves
• A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
• Calibrating approximate Bayesian credible intervals of gravitational-wave parameters
• Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
• Transformers for scientific data: a pedagogical review for astronomers
• Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings

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• RADAR-Radio Afterglow Detection and AI-driven Response: A Federated Framework for Gravitational Wave Event Follow-Up
• Learning to detect continuous gravitational waves: an open data-analysis competition
• Application of Non-Linear Noise Regression in the Virgo Detector
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• APRIL: Auxiliary Physically-Redundant Information in Loss -- A physics-informed framework for parameter estimation with a gravitational-wave case study
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