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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

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.IMgr-qc
keywords kagrabinarycoalescencescompactdetectorfeaturesimprovedinspiral
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abstract

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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Cited by 3 Pith papers

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  1. Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation

    gr-qc 2025-07 conditional novelty 6.0 of 10

    A machine learning classifier trained on the extended noise environment around gravitational wave candidates improves search sensitivity for heavy, unequal-mass black hole mergers by up to roughly 20 percent.

  2. A neural network for estimating compact binary coalescence parameters of gravitational-wave events in real time

    gr-qc 2025-05 conditional novelty 6.0 of 10

    A quantile regression neural network produces real-time credible intervals for chirp mass, mass ratio, and total mass of compact binary mergers, with coverage mostly above 90%, and these intervals serve as priors that...

  3. All-sky search for short gravitational-wave bursts in the first part of the fourth LIGO-Virgo-KAGRA observing run

    astro-ph.HE 2025-07 conditional novelty 4.0 of 10

    An all-sky search of O4a LIGO data finds no new gravitational-wave bursts and improves burst sensitivity and rate limits by factors of 2 to 10 over the previous run.

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