Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Abstract

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches where typical analyses have taken between 6 hours and 5 days. For binary neutron star and neutron star black hole systems prompt counterpart electromagnetic (EM) signatures are expected on timescales of 1 second -- 1 minute and the current fastest method for alerting EM follow-up observers, can provide estimates in $\mathcal{O}(1)$ minute, on a limited range of key source parameters. Here we show that a conditional variational autoencoder pre-trained on binary black hole signals can return Bayesian posterior probability estimates. The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution $\sim 6$ orders of magnitude faster than existing techniques.

13 pages, 5 figures

References in corpus (13)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• GW190425: Observation of a Compact Binary Coalescence with Total Mass $\sim 3.4M_{\odot }$
• The frontier of simulation-based inference
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Observation of gravitational waves from two neutron star-black hole coalescences
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• BayesLine: Bayesian Inference for Spectral Estimation of Gravitational Wave Detector Noise
• Gravitational-wave parameter estimation with autoregressive neural network flows
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Accelerating parameter inference with graphics processing units
• Conditional Variational Autoencoder for Neural Machine Translation
• Limiting the effects of earthquakes on gravitational-wave interferometers

Cited by in corpus (103)

• Gravitational-wave parameter estimation with autoregressive neural network flows
• Nested Sampling with Normalising Flows for Gravitational-Wave Inference
• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
• All-sky search for continuous gravitational waves from isolated neutron stars using Advanced LIGO and Advanced Virgo O3 data
• The Science of the Einstein Telescope
• Bilby-MCMC: An MCMC sampler for gravitational-wave inference
• Applications and Techniques for Fast Machine Learning in Science
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• Real-time gravitational-wave inference for binary neutron stars using machine learning
• Importance nested sampling with normalising flows
• Detection of Gravitational Waves Using Bayesian Neural Networks
• Normalizing flows as an avenue to study overlapping gravitational wave signals
• Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA
• Space-based gravitational wave signal detection and extraction with deep neural network
• From One to Many: A Deep Learning Coincident Gravitational-Wave Search
• VARAHA: A Fast Non-Markovian sampler for estimating Gravitational-Wave posteriors
• Solving Bayesian Inverse Problems via Variational Autoencoders
• Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
• Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• An Infrared Search for Kilonovae with the WINTER Telescope. I. Binary Neutron Star Mergers
• An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
• Accelerating Multi-Model Bayesian Inference, Model Selection and Systematic Studies for Gravitational Wave Astronomy
• Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
• Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
• Adapting to noise distribution shifts in flow-based gravitational-wave inference
• Fast likelihood evaluation using meshfree approximations for reconstructing compact binary sources
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow
• Rapid localization of gravitational wave sources from compact binary coalescences using deep learning
• Using machine learning to parametrize postmerger signals from binary neutron stars
• PyROQ: a Python-based Reduced Order Quadrature Building Code for Fast Gravitational Wave Inference
• Pre-merger sky localization of gravitational waves from binary neutron star mergers using deep learning
• Generalised gravitational burst generation with Generative Adversarial Networks
• Density estimation with Gaussian processes for gravitational-wave posteriors
• Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders
• Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows
• Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors
• Real-Time Detection of Unmodelled Gravitational-Wave Transients Using Convolutional Neural Networks
• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
• Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources
• Carnegie Supernova Project-II: Near-infrared spectral diversity and template of Type Ia Supernovae
• Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
• Swift sky localization of gravitational waves using deep learning seeded importance sampling
• Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization
• Nonparametric Representation of Neutron Star Equation of State Using Variational Autoencoder
• Prompt sky localization of compact binary sources using a meshfree approximation
• Neural post-Einsteinian framework for efficient theory-agnostic tests of general relativity with gravitational waves
• Rapid inference and comparison of gravitational-wave population models with neural variational posteriors
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Matched filtering for gravitational wave detection without template bank driven by deep learning template prediction model bank
• Localization of gravitational waves using machine learning
• Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
• Simulation-based inference of black hole ringdowns in the time domain
• Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
• Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach
• Deep learning for intermittent gravitational wave signals
• Assessing the impact of non-Gaussian noise on convolutional neural networks that search for continuous gravitational waves
• Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network
• A novel stacked hybrid autoencoder for imputing LISA data gaps
• Deep learning to detect gravitational waves from binary close encounters: Fast parameter estimation using normalizing flows
• Pinpointing coalescing binary neutron star sources with the IGWN, including LIGO-Aundha
• Conditional Noise Deep Learning for Parameter Estimation of Gravitational Wave Events
• Time delay interferometry with minimal null frequencies
• Searching for gravitational waves from stellar-mass binary black holes early inspiral
• Convolutional Neural Networks for signal detection in real LIGO data
• Reconstruction of binary black hole harmonics in LIGO using deep learning
• Binary Neutron Star Merger Search Pipeline Powered by Deep Learning
• Parameter inference for coalescing massive black hole binaries using deep learning
• Navigating Unknowns: Deep Learning Robustness for Gravitational Wave Signal Reconstruction
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Beyond GWTC-3: Analysing and verifying new gravitational-wave events from community catalogues
• Accelerating Bayesian Sampling for Massive Black Hole Binaries with Prior Constraints from Conditional Variational Autoencoder
• AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers
• Fast and accurate parameter estimation of high-redshift sources with the Einstein Telescope
• Waging a Campaign: Results from an Injection-Recovery Study involving 35 numerical Relativity Simulations and three Waveform Models
• Comparison of neural network architectures for feature extraction from binary black hole merger waveforms
• Nonlinear noise regression in gravitational-wave detectors with convolutional neural networks
• Accelerating LISA inference with Gaussian processes
• Generative adversarial network for stellar core-collapse gravitational waves
• A practical Bayesian method for gravitational-wave ringdown analysis with multiple modes
• Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows
• RADAR-Radio Afterglow Detection and AI-driven Response: A Federated Framework for Gravitational Wave Event Follow-Up
• Accelerated inference of microlensed gravitational waves with machine learning
• The impact of precession and higher-order multipoles for gravitational wave cosmological inference
• Group equivariant neural posterior estimation
• Learning to detect continuous gravitational waves: an open data-analysis competition
• Flexible Gravitational-Wave Parameter Estimation with Transformers
• Genetic-algorithm-optimized neural networks for gravitational wave classification
• Reconsidering the consistent use of precessing, higher order multipole models for gravitational wave analyses
• The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference
• Parameter inference of millilensed gravitational waves using neural spline flows
• Variational autoencoder for generating realistic $N$-body simulations for dark matter halos
• Diffusion Model-based Parameter Estimation in Dynamic Power Systems
• Lightweight posterior construction for gravitational-wave catalogs with the Kolmogorov-Arnold network
• labrador: A domain-optimized machine-learning tool for gravitational wave inference
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
• Denoising gravitational wave with deep learning in the time-frequency domain
• Auto-encoder model for faster generation of effective one-body gravitational waveform approximations