Improved binary black hole searches through better discrimination against noise transients

TL;DR

This paper introduces an improved chi-squared noise discrimination method for gravitational wave searches, significantly enhancing the detection of binary black hole signals amidst noise transients in LIGO data.

Contribution

The authors develop and validate an optimized sine-Gaussian chi-squared statistic that better distinguishes noise from signals in real LIGO data, especially for high-mass binary black hole mergers.

Findings

Improves true positive rate by ~6% in lower-mass bin.

Enhances detection efficiency by over 20% compared to traditional chi-squared.

Effective in reducing noise transients impact on high-mass CBC searches.

Abstract

Short-duration noise transients in LIGO and Virgo detectors significantly affect the search sensitivity of compact binary coalescence (CBC) signals, especially in the high mass region. In a previous work by the authors \cite{Joshi_2021}, a $\chi^2$ statistic was proposed to distinguish them, when modeled as sine-Gaussians, from non-spinning CBCs. The present work is an extension where we demonstrate the better noise-discrimination of an improved $\chi^2$ statistic -- called the optimized sine-Gaussian $\chi^2$ -- in real LIGO data. The extension includes accounting for the initial phase of the noise transients and use of a well-informed choice of sine-Gaussian basis vectors selected to discern how CBC signals and some of the most worrisome noise-transients project differently on them~\cite{sunil_2022}. To demonstrate this improvement, we use data with blip glitches from the third observational run (O3) of LIGO-Hanford and LIGO-Livingston detectors. Blips are a type of short-duration non-Gaussian noise disturbance known to adversely affect high-mass CBC searches. For CBCs, spin-aligned binary black hole signals were simulated using the \textsc{IMRPhenomPv2} waveform and injected into real LIGO data from the same run. We show that in comparison to the sine-Gaussian $\chi^2$, the optimized sine-Gaussian $\chi^2$ improves the overall true positive rate by around 6\% in a lower-mass bin ($m_1,m_2 \in [20,40]M_{\odot}$) and by more than 3\% in a higher-mass bin ($m_1,m_2 \in [60,80]M_{\odot}$). On the other hand, we see a larger improvement -- of more than 20\% -- in both mass bins in comparison to the traditional $\chi^2$.