Improved Binary Black Hole Search Discriminator from the Singular Value Decomposition of Non-Gaussian Noise Transients

TL;DR

This paper introduces a new chi-square discriminator for gravitational wave detectors that uses singular value decomposition of real glitch data to better distinguish between true signals and noise transients, improving detection reliability.

Contribution

The novel approach constructs chi-square discriminators directly from real glitch data using SVD, enhancing the ability to differentiate signals from non-Gaussian noise transients in gravitational wave detection.

Findings

The new chi-square performs as well as previous sine-Gaussian based methods.

The method effectively models glitches using real data.

Supports extension to less well-modeled glitches.

Abstract

The sensitivity of current gravitational wave (GW) detectors to transient GW signals is severely affected by a variety of non-Gaussian and non-stationary noise transients, such as the blip, tomte, koi fish, and low-frequency blip 'glitches'. These glitches share some time-frequency resemblance with GW signals from binary black holes. In earlier works [Joshi et al., Phys. Rev. D 103, 044035 (2021); Choudhary et al., Phys. Rev. D 110, 044051 (2024)], the authors presented a method for constructing a $\chi^2$-distributed optimized statistic, based on the unified formalism of $\chi^2$ discriminators [Dhurandhar et al., Phys. Rev. D 96, 103018 (2017)], to distinguish the blip glitches from the compact binary coalescence (CBC) signals. Unlike past works, the new $\chi^2$ discriminator is constructed from the most significant singular vectors obtained from the singular value decomposition of different classes of glitches in real detector data. We find that the chi-square developed in this work performs as efficiently as in Choudhary et al. [Phys. Rev. D 110, 044051 (2024)], which used sine-Gaussian basis vectors. This result supports past empirical findings that these glitches are reasonably well-modeled by sine-Gaussians. It also introduces a method for constructing signal- and glitch-based $\chi^2$ discriminators by directly using real data containing the glitches and, thus, holds promise for extensions to glitches that are captured less well by sine-Gaussians or other analytical functions.