QoQ: a Q-transform based test for gravitational wave transient events

Siddharth Soni, Ethan Marx, Erik Katsavounidis, Reed Essick, G. S. Cabourn Davies, Patrick Brockill, Michael W. Coughlin, Shaon Ghosh, Patrick Godwin

Research output: Contribution to journalArticlepeer-review


The observation of transient gravitational waves (GWs) is hindered by the presence of transient noise, colloquially referred to as glitches. These glitches can often be misidentified as GWs by searches for unmodeled transients using the excess-power type of methods and sometimes even excite template waveforms for compact binary coalescences while using matched filter techniques. They thus create a significant background in the searches. This background is more critical in getting identified promptly and efficiently within the context of real-time searches for GW transients. Such searches are the ones that have enabled multi-messenger astrophysics with the start of the Advanced LIGO and Advanced Virgo data taking in 2015 and they will continue to enable the field for further discoveries. With this work we propose and demonstrate the use of a signal-based test that quantifies the fidelity of the time-frequency decomposition of the putative signal based on first principles on how astrophysical transients are expected to be registered in the detectors and empirically measuring the instrumental noise. It is based on the Q-transform and a measure of the occupancy of the corresponding time-frequency pixels over select time-frequency volumes; we call it ‘QoQ’. Our method shows a 40% reduction in the number of retraction of public alerts that were issued by the LIGO-Virgo-KAGRA collaborations during the third observing run with negligible loss in sensitivity. Receiver Operator Characteristic measurements suggest the method can be used in online and offline searches for transients, reducing their background significantly.

Original languageEnglish
Article number015012
JournalClassical and Quantum Gravity
Issue number1
StatePublished - 4 Jan 2024


  • data quality tool
  • gravitational wave
  • signal vs noise classifier
  • transient noise


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