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Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences

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arxiv 2305.06286 v2 pith:PKCMYTDK submitted 2023-05-10 astro-ph.IM gr-qc

Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences

Leo Tsukada , Prathamesh Joshi , Shomik Adhicary , Richard George , Andre Guimaraes , Chad Hanna , Ryan Magee , Aaron Zimmerman
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Pratyusava Baral Amanda Baylor Kipp Cannon Sarah Caudill Bryce Cousins Jolien D. E. Creighton Becca Ewing Heather Fong Patrick Godwin Reiko Harada Yun-Jing Huang Rachael Huxford James Kennington Soichiro Kuwahara Alvin K. Y. Li Duncan Meacher Cody Messick Soichiro Morisaki Debnandini Mukherjee Wanting Niu Alex Pace Cort Posnansky Anarya Ray Surabhi Sachdev Shio Sakon Divya Singh Ron Tapia Takuya Tsutsui Koh Ueno Aaron Viets Leslie Wade Madeline Wade
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classification astro-ph.IM gr-qc
keywords kagrabinarycoalescencescompactdetectorfeaturesimprovedinspiral
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration is planning to conduct the fourth observing run with improved detector sensitivities and an expanded detector network including KAGRA. Accordingly, it is vital to optimize the detection algorithm of low-latency search pipelines, increasing their sensitivities to gravitational waves from compact binary coalescences. In this work, we discuss several new features developed for ranking statistics of GstLAL-based inspiral pipeline, which mainly consist of: the signal contamination removal, the bank-$\xi^2$ incorporation, the upgraded $\rho-\xi^2$ signal model and the integration of KAGRA. An injection study demonstrates that these new features improve the pipeline's sensitivity by approximately 15% to 20%, paving the way to further multi-messenger observations during the upcoming observing run.

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