2024/07/26 by Chatterjee, Deep, Marx, Ethan, Benoit, William +12 · 3 citations
#FOS: Computer and information sciences #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2407.19048
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 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 \lesssim 24 hours. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of ∼ 6s.