Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA
S. Sakon, Leo Tsukada, Heather Fong, James Kennington, Wanting Niu, Chad Hanna, Shomik Adhicary, Pratyusava Baral, A. C. Baylor, K. C. Cannon, Sarah Caudill, B. Cousins, J. D. E. Creighton, Becca Ewing, J. George, P. Godwin, Reiko Harada, Yun-Jing Huang, R. Huxford, Prathamesh Joshi, Soichiro Kuwahara, Alvin K. Y. Li, R. M. Magee, D. Meacher, C. Messick, S. Morisaki, Debnandini Mukherjee, Alex Pace, Cort Posnansky, Anarya Ray, S. Sachdev, Divya Singh, Ron Tapia, T. Tsutsui, K. Ueno, A. D. Viets, L. E. Wade, M. Wade, Jonathan Wang
Abstract
Matched-filtering gravitational-wave search pipelines identify gravitational-wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational-wave detector data and gravitational-wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational-wave waveforms are often stored in ``template banks,'' and the construction of a densely populated template bank is essential for some gravitational-wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, manifold. The template bank contains $1.8\ifmmode\times\else\texttimes\fi{}{10}^{6}$ sets of template parameters covering plausible neutron star and black hole systems up to a total mass of $400{M}_{\ensuremath{\bigodot}}$ with component masses between $1--200{M}_{\ensuremath{\bigodot}}$ and mass ratios between 1 and 20 under the assumption that each component object's angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, manifold, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The GstLAL search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly 5 times as compared to previously reported SVD sorting schemes.