PREPRINT

Deep Residual Networks for Gravitational Wave Detection

Paraskevi Nousi, Alexandra E. Koloniari, Nikolaos Passalis, Panagiotis Iosif, Nikolaos Stergioulas, Anastasios Tefas

Submitted on 2 November 2022

Abstract

Traditionally, gravitational waves are detected with techniques such as matched filtering or unmodeled searches based on wavelets. However, in the case of generic black hole binaries with non-aligned spins, if one wants to explore the whole parameter space, matched filtering can become impractical, which sets severe restrictions on the sensitivity and computational efficiency of gravitational-wave searches. Here, we use a novel combination of machine-learning algorithms and arrive at sensitive distances that surpass traditional techniques in a specific setting. Moreover, the computational cost is only a small fraction of the computational cost of matched filtering. The main ingredients are a 54-layer deep residual network (ResNet), a Deep Adaptive Input Normalization (DAIN), a dynamic dataset augmentation, and curriculum learning, based on an empirical relation for the signal-to-noise ratio. We compare the algorithm's sensitivity with two traditional algorithms on a dataset consisting of a large number of injected waveforms of non-aligned binary black hole mergers in real LIGO O3a noise samples. Our machine-learning algorithm can be used in upcoming rapid online searches of gravitational-wave events in a sizeable portion of the astrophysically interesting parameter space. We make our code, AResGW, and detailed results publicly available at https://github.com/vivinousi/gw-detection-deep-learning.

Preprint

Comment: 10 pages, 11 figures, code publicly available at https://github.com/vivinousi/gw-detection-deep-learning

Subjects: General Relativity and Quantum Cosmology; Astrophysics - High Energy Astrophysical Phenomena

URL: http://arxiv.org/abs/2211.01520

A representative 2-channel data segment of the training set containing an injection in real O3a noise from the Hanford (H1) detector (left panel) and the Livingston (L1) detector (right panel). The {\it whitened strain} of a 1 s segment around the time of coalescence is shown. The coalescence times in the detector frames are within the 0.5 s - 0.7 s range (shown as a shaded area). The injected waveform is shown scaled to match the difference between the whitened foreground and background segments. In this example, the component masses are $m_1=27.74M_\odot$, $m_2=11.50M_\odot$ and the luminosity distance is $d=1497 Mpc$. The {\it non-aligned spins} have magnitudes of 0.624 and 0.008, respectively.