Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors

Gregory Ashton*, Sarah Thiele, Yannick Lecoeuche, Jess McIver, Laura K. Nuttall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Downloads (Pure)

Abstract

The two interferometric LIGO gravitational-wave observatories provide the most sensitive data to date to study the gravitational-wave universe. As part of a global network, they have completed their third observing run in which they observed many tens of signals from merging compact binary systems. It has long been known that a limiting factor in identifying transient gravitational-wave signals is the presence of transient non-Gaussian noise, which reduce the ability of astrophysical searches to detect signals confidently. Significant efforts are taken to identify and mitigate this noise at the source, but its presence persists, leading to the need for software solutions. Taking a set of transient noise artefacts categorised by the GravitySpy software during the O3a observing era, we produce parameterised population models of the noise projected into the space of astrophysical model parameters of merging binary systems. We compare the inferred population properties of transient noise artefacts with observed astrophysical systems from the GWTC2.1 catalogue. We find that while the population of astrophysical systems tend to have near equal masses and moderate spins, transient noise artefacts are typically characterised by extreme mass ratios and large spins. This work provides a new method to calculate the consistency of an observed candidate with a given class of noise artefacts. This approach could be used in assessing the consistency of candidates found by astrophysical searches (i.e. determining if they are consistent with a known glitch class). Furthermore, the approach could be incorporated into astrophysical searches directly, potentially improving the reach of the detectors, though only a detailed study would verify this.

Original languageEnglish
Article number175004
Number of pages23
JournalClassical and Quantum Gravity
Volume39
Issue number17
DOIs
Publication statusPublished - 9 Aug 2022

Keywords

  • black holes
  • data analysis
  • gravitational-waves
  • UKRI
  • STFC
  • MR/T01881X/1

Fingerprint

Dive into the research topics of 'Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors'. Together they form a unique fingerprint.

Cite this