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Increasing GstLAL search sensitivity through iDQ binning scheme

ORAL

Abstract

iDQ is a data quality pipeline that can detect transient noise in gravitational wave data by using each interferometer's auxiliary data. During the LVK's third observational run the GstLAL pipeline used iDQ to downrank single detector candidates when noise was non-Gaussian, therefore improving single detector sensitivity. We propose to incorporate iDQ information at the background collection stage. This will be helpful in mitigating GstLAL's unnecessary penalization of stretches of data that happen to be coincident with a less noisy background. I will be discussing our methods and improvements to GstLAL's sensitivity.

Presenters

  • Richard George

    University of Texas at Austin

Authors

  • Richard George

    University of Texas at Austin

  • RYAN MAGEE

    LIGO Laboratory, Caltech

  • Alvin Ka Yue Li

    LIGO Laboratory, Caltech

  • Rachael Huxford

    Pennsylvania State University