Presentation #348.05 in the session Gravitational Wave and Multi-messenger Astronomy — iPoster Session.
The Laser Interferometer Gravitational-Wave Observatory (LIGO) and its international partners Virgo and KAGRA observe and study astrophysical events that create gravitational waves detectable on Earth. During the process of data acquisition, transient noise from environmental and instrumental sources causes glitches in the data. The impact of these glitches on the search for gravitational waves can be mitigated by removing (vetoing) these glitches from the data. Omicron is an algorithm that is currently used to automatically identify data segments likely to be glitches. Two algorithms are also used that automate the evaluation data quality when glitches originating from different auxiliary channels are vetoed: hierarchical veto (Hveto) ranks the significance of the glitch segments coincident between the gravitational wave data and auxiliary channel data while the Used Percentage Veto (UPV) ranks based on which channel’s vetoes are used the most. The highest ranked auxiliary channel is identified as a veto channel and removed from further consideration for both Hveto and UPV. This process is repeated until the highest ranked auxiliary channel fails to meet a significance threshold.
This research focuses on applying these automated veto tools to evaluate their potential to improve the search for burst (unmodelled) gravitational waves. To do this, software has been developed to collect the daily results of Hveto and UPV and apply those candidate vetoes to the flagship burst search algorithm Coherent WaveBurst (cWB). This evaluation is performed by the Veto Evaluation Tool (VET) to measure a veto’s efficiency in removing cWB triggers, deadtime (amount of time removed by the veto), and the ratio of efficiency to deadtime. The higher this ratio, the more effective the veto. Ultimately, this will be integrated onto the LIGO summary webpages, a data quality utility that is available to the entire LIGO collaboration.
The early results of this application to the burst search show that Hveto and UPV provide vetoes that identify unique glitch features, and both have the potential to improve data quality for burst gravitational wave search.