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Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves

Davis, Derek and Trevor, Max and Mozzon, Simone and Nuttall, Laura K. (2022) Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220712-193855803

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Abstract

We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coalescences. We use information from LIGO data quality streams that monitor the status of each detector and its environment to model changes in the rate of noise in each detector. These data quality streams allow candidates identified in the data during periods of detector malfunctions to be more efficiently rejected as noise. This method allows data from machine learning predictions of the detector state to be included as part of the PyCBC search, increasing the the total number of detectable gravitational-wave signals by up to 5%. When both machine learning classifications and manually-generated flags are used to search data from LIGO-Virgo's third observing run, the total number of detectable gravitational-wave signals is increased by up to 20% compared to not using any data quality streams. We also show how this method is flexible enough to include information from large numbers of additional arbitrary data streams that may be able to further increase the sensitivity of the search.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2204.03091arXivDiscussion Paper
ORCID:
AuthorORCID
Davis, Derek0000-0001-5620-6751
Nuttall, Laura K.0000-0002-8599-8791
Additional Information:The authors thank the LIGO-Virgo-KAGRA PyCBC and Detector Characterization groups for their input and suggestions during the development of this work. We would like to thank Patrick Godwin for productive discussions on how to best utilize iDQ time series data, and to Gareth Cabourn Davies for their comments during internal review of this paper. DD is supported by the NSF as a part of the LIGO Laboratory. MT is supported by the NSF through grant PHY-2012159. SM is supported by a STFC studentship. LKN thanks the UKRI Future Leaders Fellowship for support through the grant MR/T01881X/1. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation, and operates under cooperative agreement PHY-1764464. Advanced LIGO was built under award PHY-0823459. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This work carries LIGO document number P2200078.
Group:LIGO
Funders:
Funding AgencyGrant Number
NSFPHY-2012159
Science and Technology Facilities Council (STFC)UNSPECIFIED
UK Research and InnovationMR/T01881X/1
NSFPHY-1764464
NSFPHY-0823459
NSFPHY-0757058
NSFPHY-0823459
Other Numbering System:
Other Numbering System NameOther Numbering System ID
LIGO DocumentP2200078
Record Number:CaltechAUTHORS:20220712-193855803
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220712-193855803
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115504
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:13 Jul 2022 21:26
Last Modified:13 Jul 2022 21:26

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