Identifying correlations between LIGO's astronomical range and auxiliary sensors using lasso regression

TL;DR

This paper presents a lasso regression-based algorithm to identify auxiliary sensors correlated with LIGO's observational range, aiming to improve noise reduction and enhance gravitational wave detection.

Contribution

It introduces a novel application of lasso regression to analyze auxiliary channels for noise source identification in LIGO data.

Findings

Successfully identified key auxiliary channels correlated with LIGO's range.

Demonstrated the method's effectiveness on multiple LIGO Livingston data periods.

Discussed computational performance and current limitations of the approach.

Abstract

The range to which the Laser Interferometer Gravitational-Wave Observatory (LIGO) can observe astrophysical systems varies over time, limited by noise in the instruments and their environments. Identifying and removing the sources of noise that limit LIGO's range enables higher signal-to-noise observations and increases the number of observations. The LIGO observatories are continuously monitored by hundreds of thousands of auxiliary channels that may contain information about these noise sources. This paper describes an algorithm that uses linear regression, namely lasso (least absolute shrinkage and selection operator) regression, to analyze all of these channels and identify a small subset of them that can be used to reconstruct variations in LIGO's astrophysical range. Exemplary results of the application of this method to three different periods of LIGO Livingston data are presented, along with computational performance and current limitations.