[en] Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of waveform models that link between signal shapes and the source parameters, running Markov chains until convergence is typically expensive and requires days of computation. In this extended abstract, we provide a proof of concept that demonstrates how the latest advances in neural simulation-based inference can speed up the inference time by up to three orders of magnitude – from days to minutes – without impairing the performance. Our approach is based on a convolutional neural network modeling the likelihood-to-evidence ratio and entirely amortizes the computation of the posterior. We find that our model correctly estimates credible intervals for the parameters of simulated gravitational waves.
Disciplines :
Space science, astronomy & astrophysics Computer science
Author, co-author :
Delaunoy, Arnaud ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes microélectroniques intégrés
Wehenkel, Antoine ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
Hinderer, Tanja
Nissanke, Samaya
Weniger, Christoph
Williamson, Andrew
Louppe, Gilles ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
Language :
English
Title :
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Publication date :
11 December 2020
Number of pages :
5
Event name :
Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS)