Mitigating the impact of noise transients in gravitational-wave searches using reduced basis timeseries and convolutional neural networks

TL;DR

This paper introduces a novel neural network-based method using reduced basis timeseries to effectively distinguish gravitational-wave signals from noise transients, improving detection accuracy in gravitational-wave data analysis.

Contribution

It presents a new signal consistency check employing basis spans and CNNs for glitch identification, enhancing gravitational-wave detection pipelines.

Findings

CNN classifies over 99% of responses accurately

Higher true positive rate compared to toy detection pipeline

Including CNN information increases detection sensitivity

Abstract

Gravitational-wave detection pipelines have helped to identify over one hundred compact binary mergers in the data collected by the Advanced LIGO and Advanced Virgo interferometers, whose sensitivity has provided unprecedented access to the workings of the gravitational universe. The detectors are, however, subject to a wide variety of noise transients (or glitches) that can contaminate the data. Although detection pipelines utilize a variety of noise mitigation techniques, glitches can occasionally bypass these checks and produce false positives. One class of mitigation techniques is the signal consistency check, which aims to quantify how similar the observed data is to the expected signal. In this work, we describe a new signal consistency check that utilizes a set of bases that spans the gravitational-wave signal space and convolutional neural networks (CNN) to probabilistically identify glitches. We recast the basis response as a grayscale image, and train a CNN to distinguish between gravitational-waves and glitches with similar morphologies. We find that the CNN accurately classifies $\gtrsim 99\%$ of the responses it is shown. We compare these results to a toy detection pipeline, finding that the two methods produce similar false positive rates, but that the CNN has a significantly higher true positive rate. We modify our toy model detection pipeline and demonstrate that including information from the network increases the toy pipeline's true positive rate by $4-7\%$ while decreasing the false positive rate to a data-limited bound of $\lesssim 0.1\%$.