Published January 15, 2022 | Published + Submitted
Journal Article Open

SPIIR online coherent pipeline to search for gravitational waves from compact binary coalescences

Abstract

This paper presents the Summed Parallel Infinite Impulse Response (SPIIR) pipeline used for public alerts during the third advanced LIGO and Virgo observation run (O3 run). The SPIIR pipeline uses infinite impulse response (IIR) filters to perform extremely low-latency matched filtering and this process is further accelerated with graphics processing units (GPUs). It is the first online pipeline to select candidates from multiple detectors using a coherent statistic based on the maximum network likelihood ratio statistic principle. Here we simplify the derivation of this statistic using the singular-value-decomposition (SVD) technique and show that single-detector signal-to-noise ratios from matched filtering can be directly used to construct the statistic. Coherent searches are in general more computationally challenging than coincidence searches due to extra search over sky direction parameters. The search over sky directions follows an embarrassing parallelization paradigm and has been accelerated using GPUs. The detection performance is reported using a segment of public data from LIGO-Virgo's second observation run. We demonstrate that the median latency of the SPIIR pipeline is less than 9 seconds, and present an achievable road map to reduce the latency to less than 5 seconds. During the O3 online run, SPIIR registered triggers associated with 38 of the 56 nonretracted public alerts. The extreme low-latency nature makes it a competitive choice for joint time-domain observations, and offers the tantalizing possibility of making public alerts prior to the merger phase of binary coalescence systems involving at least one neutron star.

Additional Information

© 2022 American Physical Society. (Received 13 November 2020; accepted 30 November 2021; published 6 January 2022) This work was funded by the Australian Research Council (ARC) Centre of Excellence for Gravitational Wave Discovery OzGrav under Grant No. CE170100004. K. K. is partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. NRF-2020R1C1C1005863). T. G. F. L. was partially supported by grants from the Research Grants Council of the Hong Kong (Project No. CUHK14306419 and No. CUHK14306218), Research Committee of the Chinese University of Hong Kong and the Croucher Foundation of Hong Kong. A. Sengupta thanks the Department of Science and Technology for their ICPS cluster Grant No. DST/ICPS/Cluster/Data_Science/2018/General/T-150. We wish to acknowledge Tom Almeida, Andrew Munt, Zhaohong Peng, Han-Shiang Kuo, Fengli Lin, Guo Chin Liu for helpful discussions on the improvement of the work. This work used the computer resources of the LIGO CIT (Caltech) computer cluster and OzStar computer cluster at Swinburne University of Technology. LIGO CIT cluster is funded by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. The OzSTAR program receives funding in part from the Astronomy National Collaborative Research Infrastructure Strategy (NCRIS) allocation provided by the Australian Government. We wish to thank Stuart Anderson, Jarrod Hurley for the great help to use the clusters. This research used data obtained from the Gravitational Wave Open Science Center [58], a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This research used the injection sets generated by the rates and population group of the LIGO Scientific Collaboration. We acknowledge the gstlal Team for the gstlal library for several modules used in this work.

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Published - PhysRevD.105.024023.pdf

Submitted - 2011.06787.pdf

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Additional details

Created:
September 15, 2023
Modified:
October 23, 2023