Rapid likelihood free inference of compact binary coalescences using accelerated hardware
Deep Chatterjee, Ethan Marx, W. Benoit, Ravi Kumar, Malina Desai, Ekaterina Govorkova, Alec Gunny, Eric A. Moreno, Rafia Omer, Ryan Jonathan Raikman, M. Saleem, Shrey Aggarwal, M. W. Coughlin, Philip Coleman Harris, E. Katsavounidis · Machine Learning Science and Technology · 2024
Abstract 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 ≲ 24 h. 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 ∼ 6 s.