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

Understand

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%.