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