Rapid likelihood free inference of compact binary coalescences using accelerated hardware

Rapid likelihood free inference of compact binary coalescences using accelerated hardware

We report a gravitational-wave parameter estimation algorithm, AMPLFI , based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe . We presen...

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Main Authors: D Chatterjee, E Marx, W Benoit, R Kumar, M Desai, E Govorkova, A Gunny, E Moreno, R Omer, R Raikman, M Saleem, S Aggarwal, M W Coughlin, P Harris, E Katsavounidis
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
Likelihood-free Inference
Self-supervised learning
Gravitational waves
Multi-messenger astronomy
Online Access:https://doi.org/10.1088/2632-2153/ad8982
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https://doi.org/10.1088/2632-2153/ad8982

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