Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves

Derek Davis, Max Trevor, Simone Mozzon, Laura K. Nuttall

Research output: Contribution to journalArticlepeer-review

Abstract

We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coalescences. We use information from LIGO data quality streams that monitor the status of each detector and its environment to model changes in the rate of noise in each detector. These data quality streams allow candidates identified in the data during periods of detector malfunctions to be more efficiently rejected as noise. This method allows data from machine learning predictions of the detector state to be included as part of the PyCBC search, increasing the the total number of detectable gravitational-wave signals by up to 5%. When both machine learning classifications and manually-generated flags are used to search data from LIGO-Virgo's third observing run, the total number of detectable gravitational-wave signals is increased by up to 20% compared to not using any data quality streams. We also show how this method is flexible enough to include information from large numbers of additional arbitrary data streams that may be able to further increase the sensitivity of the search.
Original languageEnglish
Article number102006
Number of pages14
JournalPhysical Review D
Volume106
Issue number10
Early online date15 Nov 2022
DOIs
Publication statusPublished - 22 Nov 2022

Keywords

  • UKRI
  • STFC

Fingerprint

Dive into the research topics of 'Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves'. Together they form a unique fingerprint.

Cite this