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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 has6million trainable parameters with training times24h. 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 of6s.

 
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PAR ID:
10552916
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
IOP Publishing
Date Published:
Journal Name:
Machine Learning: Science and Technology
Volume:
5
Issue:
4
ISSN:
2632-2153
Format(s):
Medium: X Size: Article No. 045030
Size(s):
Article No. 045030
Sponsoring Org:
National Science Foundation
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