Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders

Abstract

All-sky searches for continuous gravitational waves are generally model dependent and computationally costly to run. By contrast, SOAP is a model-agnostic search that rapidly returns candidate signal tracks in the time-frequency plane. In this work we extend the SOAP search to return broad Bayesian posteriors on the astrophysical parameters of a specific signal model. These constraints drastically reduce the volume of parameter space that any follow-up search needs to explore, so increasing the speed at which candidates can be identified and confirmed. Our method uses a machine learning technique, specifically a conditional variational auto-encoder, and delivers a rapid estimation of the posterior distribution of the four Doppler parameters of a continuous wave signal. It does so without requiring a clear definition of a likelihood function, or being shown any true Bayesian posteriors in training. We demonstrate how the Doppler parameter space volume can be reduced by a factor of $\mathcal{O}(10^{-7})$ for signals of SNR 100.

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• Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders
• Searching for gravitational waves from stellar-mass binary black holes early inspiral
• Novel neural-network architecture for continuous gravitational waves
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Method to search for inspiraling planetary-mass ultra-compact binaries using the generalized frequency-Hough transform in LIGO O3a data
• Large-kernel Convolutional Neural Networks for Wide Parameter-Space Searches of Continuous Gravitational Waves
• Transformer Networks for Continuous Gravitational-wave Searches
• Gravitational waves from sub-solar mass primordial black holes
• Learning to detect continuous gravitational waves: an open data-analysis competition