Searches for mass-asymmetric compact binary coalescence events using neural networks in the LIGO/Virgo third observation period
M. Andrés‐Carcasona, A. Menéndez-Vázquez, M. Martı́nez, L. M. Mir
Abstract
We present the results of the search for the coalescence of compact binary mergers with very asymmetric mass configurations using convolutional neural networks and the Laser Interferometer Gravitational Wave Observatory/Virgo data for the third observation period (O3). Two-dimensional images in time and frequency are used as input. Masses in the range of $0.01\ensuremath{-}20{M}_{\ensuremath{\bigodot}}$ are considered. We explore neural networks trained with input information from a single interferometer, pairs of interferometers, or all three interferometers together, indicating that the use of the maximum information available leads to an improved performance. A scan over the O3 dataset using the convolutional neural networks for detection results in no significant excess from an only noise hypothesis. The results are translated into 90% confidence level upper limits on the merger rate as a function of the mass parameters of the binary system.