Exploring gravitational-wave detection and parameter inference using Deep Learning methods

TL;DR

This paper demonstrates that deep learning algorithms can effectively detect gravitational waves from binary black hole mergers and infer their parameters, achieving high accuracy and comparable results to traditional methods using real LIGO-Virgo data.

Contribution

The study introduces a deep learning framework for gravitational wave detection and parameter inference, showing robustness across distances and improving single detector performance by combining multi-detector data.

Findings

Deep learning detects GW signals at distances up to 2000 Mpc with high accuracy.

Combining data from three detectors enhances detection performance by up to 70%.

Parameter inference results are consistent with LIGO-Virgo published values for most events.

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 70%. Furthermore, we train a regression network to perform parameter inference on BBH spectrogram data and apply this network to the events from the the GWTC-1 and GWTC-2 catalogs. Without significant optimization of our algorithms we obtain results that are mostly consistent with published results by the LIGO-Virgo Collaboration. In particular, our predictions for the chirp mass are compatible (up to 3$\sigma$) with the official values for 90% of events