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Title: Rapid likelihood free inference of compact binary coalescences using accelerated hardware
Abstract We report a gravitational-wave parameter estimation algorithm,AMPLFI, based on likelihood-free inference using normalizing flows. The focus ofAMPLFIis to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search,Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has 6 million trainable parameters with training times 24 h. Based on online deployment on a mock data stream of LIGO-Virgo data,Aframe+AMPLFIis able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of 6 s.  more » « less
Award ID(s):
2308862 2117997
PAR ID:
10598595
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
Purpose-Led Publishing
Date Published:
Journal Name:
Machine Learning: Science and Technology
Volume:
5
Issue:
4
ISSN:
2632-2153
Page Range / eLocation ID:
045030
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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