Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences
Leo Tsukada, Prathamesh Joshi, Shomik Adhicary, Richard T. De George, Andre Guimaraes, Chad Hanna, R. M. Magee, Aaron Zimmerman, Pratyusava Baral, A. C. Baylor, K. C. Cannon, Sarah Caudill, B. Cousins, J. D. E. Creighton, Becca Ewing, Heather Fong, P. Godwin, R. Harada, Yun-Jing Huang, R. Huxford, James Kennington, S. Kuwahara, Alvin K. Y. Li, D. Meacher, C. Messick, S. Morisaki, Debnandini Mukherjee, Wanting Niu, Alex Pace, Cort Posnansky, Anarya Ray, S. Sachdev, S. Sakon, D. Singh, Ron Tapia, T. Tsutsui, K. Ueno, A. D. Viets, L. E. Wade, M. Wade
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-${\ensuremath{\xi}}^{2}$ incorporation, the upgraded $\ensuremath{\rho}\ensuremath{-}{\ensuremath{\xi}}^{2}$ 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 multimessenger observations during the upcoming observing run.