English

New Methods for Offline GstLAL Analyses

General Relativity and Quantum Cosmology 2026-05-12 v3 Instrumentation and Methods for Astrophysics

Abstract

In this work, we present new methods implemented in the GstLAL offline gravitational wave search. These include a technique to reuse the matched filtering data products from a GstLAL online analysis, which hugely reduces the time and computational resources required to obtain offline results; a technique to combine these results with a separate search for heavier black hole mergers, enabling detections from a larger set of gravitational wave sources; changes to the likelihood ratio which increases the sensitivity of the analysis; and two separate changes to the background estimation, allowing more precise significance estimation of gravitational wave candidates. Some of these methods increase the sensitivity of the analysis, whereas others correct previous mis-estimations of sensitivity by eliminating false positives. These methods have been adopted for GstLAL's offline results during the fourth observing run of LIGO, Virgo, and KAGRA (O4). To test these new methods, we perform an offline analysis over one chunk of O3 data, lasting from May 12 19:36:42 UTC 2019 to May 21 14:45:08 UTC 2019, and compare it with previous GstLAL results over the same period of time. We show that cumulatively these methods afford around a 50% - 100% increase in sensitivity in the highest mass space, while simultaneously increasing the reliability of results, and making them more reusable and computationally cheaper.

Keywords

Cite

@article{arxiv.2506.06497,
  title  = {New Methods for Offline GstLAL Analyses},
  author = {Prathamesh Joshi and Leo Tsukada and Chad Hanna and Shomik Adhicary and Debnandini Mukherjee and Wanting Niu and Shio Sakon and Divya Singh and Pratyusava Baral and Amanda Baylor and Kipp Cannon and Sarah Caudill and Bryce Cousins and Jolien D. E. Creighton and Becca Ewing and Heather Fong and Richard N. George and Patrick Godwin and Reiko Harada and Yun-Jing Huang and Rachael Huxford and James Kennington and Soichiro Kuwahara and Alvin K. Y. Li and Ryan Magee and Duncan Meacher and Cody Messick and Soichiro Morisaki and Alexander Pace and Cort Posnansky and Anarya Ray and Surabhi Sachdev and Stefano Schmidt and Urja Shah and Ron Tapia and Koh Ueno and Aaron Viets and Leslie Wade and Madeline Wade and Zach Yarbrough and Noah Zhang},
  journal= {arXiv preprint arXiv:2506.06497},
  year   = {2026}
}

Comments

16 pages, 9 figures, 4 tables

R2 v1 2026-07-01T03:04:23.639Z