SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients

Understand

Blip glitches, a type of short-duration noise transient in the LIGO--Virgo data, are a nuisance for the binary black hole (BBH) searches.

• They affect the BBH search sensitivity significantly because their time-domain morphologies are very similar, and that creates difficulty in vetoing them.
• In this work, we construct a deep-learning neural network to efficiently distinguish BBH signals from blip glitches.
• We introduce sine-Gaussian projection (SGP) maps, which are projections of GW frequency-domain data snippets on a basis of sine-Gaussians defined by the quality factor and central frequency.