Metric assisted stochastic sampling search for gravitational waves from binary black hole mergers

Chad Hanna, Prathamesh Joshi, Rachael Huxford, Kipp Cannon, Sarah Caudill, Chiwai Chan, Bryce Cousins, Jolien D.E. Creighton, Becca Ewing, Miguel Fernandez, Heather Fong, Patrick Godwin, Ryan Magee, Duncan Meacher, Cody Messick, Soichiro Morisaki, Debnandini Mukherjee, Hiroaki Ohta, Alexander Pace, Stephen PriviteraSurabhi Sachdev, Shio Sakon, Divya Singh, Ron Tapia, Leo Tsukada, Daichi Tsuna, Takuya Tsutsui, Koh Ueno, Aaron Viets, Leslie Wade, Madeline Wade, Jonathan Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We present a novel gravitational-wave detection algorithm that conducts a matched-filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler, allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC-Aug 25 22:00 2017 UTC and recover eight known confident gravitational-wave candidates. We also inject simulated binary black hole signals to demonstrate the sensitivity.

Original languageEnglish (US)
Article number084033
JournalPhysical Review D
Issue number8
StatePublished - Oct 15 2022

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics


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