Effects of Data Quality Vetoes on a Search for Compact Binary Coalescences in Advanced LIGO's First Observing Run

TL;DR

This paper demonstrates that systematic data quality vetoes significantly enhance the sensitivity of gravitational wave searches in LIGO data, notably reducing false alarms for certain signals.

Contribution

It introduces a method for applying data quality vetoes to improve search sensitivity and reduce false alarms in gravitational wave detection.

Findings

Removing noisy data improves search sensitivity.

Data vetoes greatly reduce false alarm rates.

GW150914 detection unaffected by vetoes.

Abstract

The first observing run of Advanced LIGO spanned 4 months, from September 12, 2015 to January 19, 2016, during which gravitational waves were directly detected from two binary black hole systems, namely GW150914 and GW151226. Confident detection of gravitational waves requires an understanding of instrumental transients and artifacts that can reduce the sensitivity of a search. Studies of the quality of the detector data yield insights into the cause of instrumental artifacts and data quality vetoes specific to a search are produced to mitigate the effects of problematic data. In this paper, the systematic removal of noisy data from analysis time is shown to improve the sensitivity of searches for compact binary coalescences. The output of the PyCBC pipeline, which is a python-based code package used to search for gravitational wave signals from compact binary coalescences, is used as a metric for improvement. GW150914 was a loud enough signal that removing noisy data did not improve its significance. However, the removal of data with excess noise decreased the false alarm rate of GW151226 by more than two orders of magnitude, from 1 in 770 years to less than 1 in 186000 years.