0000000000329491
AUTHOR
Joao D. Alvares
Gravitational-wave parameter inference using Deep Learning
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, and the data from each detector in the Advanced LIGO and Advanced Virgo network is combined into a unique RGB image. We show that a clas…
Exploring gravitational-wave detection and parameter inference using deep learning methods
The data that support the findings of this study are openly available at the following URL/DOI: https://arxiv.org/abs/2011.10425.