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, 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 HuxfordJames 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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration has been conducting 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-ζ2 incorporation, the upgraded ρ-ζ2 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 multimessenger observations during the upcoming observing run.

Original languageEnglish (US)
Article number043004
JournalPhysical Review D
Volume108
Issue number4
DOIs
StatePublished - Aug 15 2023

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics

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