Exploring gravitational-wave detection and parameter inference using deep learning methods

Abstract

We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals. We demonstrate that DL algorithms, trained with GW signal waveforms at distances of 2000 Mpc, still show high accuracy when detecting closer signals, within the ranges considered in our analysis. Moreover, by combining the results of the three-detector network in a unique RGB image, the single detector performance is improved by as much as * Author to whom any correspondence should be addressed.

Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

“…This would be especially beneficial for searches that require low latency, such as the early warning of binary neutron star mergers (Baltus et al, 2021 ; Yu et al, 2021 ). Other successful usage of ML techniques in GW astronomy include the identification of various GW events (Bayley et al, 2020 ; Chan et al, 2020 ; Dreissigacker and Prix, 2020 ; Huerta et al, 2020 ; Krastev, 2020 ; Schäfer et al, 2020 ; Wong et al, 2020 ; Beheshtipour and Papa, 2021 ; Chang et al, 2021 ; Chatterjee et al, 2021 ; López et al, 2021 ; Marianer et al, 2021 ; Mishra et al, 2021 ; Saiz-Pérez et al, 2021 ; Wei and Huerta, 2021 ; Yan et al, 2021 ), source parameter estimations (Gabbard et al, 2019 ; Chatterjee et al, 2020 ; Chua and Vallisneri, 2020 ; Green et al, 2020 ; Talbot and Thrane, 2020 ; Álvares et al, 2021 ; D'Emilio et al, 2021 ; Krastev et al, 2021 ; Williams et al, 2021 ; Xia et al, 2021 ), and detector characterization (Biswas et al, 2020 ; Colgan et al, 2020 ; Cuoco et al, 2020 ; Essick et al, 2020 ; Torres-Forné et al, 2020 ; Mogushi, 2021 ; Sankarapandian and Kulis, 2021 ; Soni et al, 2021 ; Zhan et al, 2021 ). Besides GW astronomy, the usage of CNNs has led to breakthroughs in a variety of topics related to time-series forecasting and classification (e.g., Refs.…”

Detecting and Denoising Gravitational Wave Signals from Binary Black Holes using Deep Learning

“…DL methods are first applied to direct detection of GWs by George and Huerta using simulated aLIGO noise [18] and further extended to real aLIGO noise, resulted in detecting the presence of the first GW, GW150914 [25] in the data stream. This work has inspired several attempts to identify and locate GWs in real aLIGO data [26][27][28][29][30][31] and many other ML based studies focused on parameter estimation of real GWs [32][33][34][35] followed. Denoising GWs using DL was applied in [36], and the proposed denoising scheme was able to extract four GW events (three from Hanford, one from Livingston) with high signal overlaps.…”

Parameter Estimation of Gravitational Waves with a Quantum Metropolis Algorithm

“…Historically, the computational advances have been very large. Over the last 20 years, different types of algorithms have been proposed (see Refs [2,3]), but those based on the formalism of Bayesian inference have stood out from the rest, making Bayesian parameter estimation the language of gravitational-wave astronomy. Among these algorithms the most important are the Metropolis-Hastings algorithms based on Monte Carlo Markov chains [4].…”