Distinguishing short duration noise transients in LIGO data to improve the PyCBC search for gravitational waves from high mass binary black hole mergers

TL;DR

This paper presents a method to distinguish blip glitches from gravitational-wave signals in LIGO data, significantly improving detection rates for high-mass binary black hole mergers during Advanced LIGO's second observing run.

Contribution

The paper introduces a straightforward noise separation technique that enhances the PyCBC search sensitivity for high-mass black hole mergers by about 20%.

Findings

Approximately 20% increase in detection rate for high-mass mergers.

Effective separation of blip glitches from gravitational-wave signals.

Improved data quality during LIGO's second observing run.

Abstract

"Blip glitches" are a type of short duration transient noise in LIGO data. The cause for the majority of these is currently unknown. Short duration transient noise creates challenges for searches of the highest mass binary black hole systems, as standard methods of applying signal consistency, which look for consistency in the accumulated signal-to-noise of the candidate event, are unable to distinguish many blip glitches from short duration gravitational-wave signals due to similarities in their time and frequency evolution. We demonstrate a straightforward method, employed during Advanced LIGO's second observing run, including the period of joint observation with the Virgo observatory, to separate the majority of this transient noise from potential gravitational-wave sources. This yields a $\sim 20\%$ improvement in the detection rate of high mass binary black hole mergers ($> 60 M_{\odot}$) for the PyCBC analysis.