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Background Filter: A method for removing signal contamination during significance estimation of a GstLAL anaysis

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arxiv 2305.18233 v1 pith:SF4WMZCM submitted 2023-05-29 gr-qc

Prathamesh Joshi , Leo Tsukada , Chad Hanna This is my paper
classification gr-qc
keywords gravitational-wavenoisebackgroundfiltergstlalsensitivitystatisticsanalysis
verification ladder T0 review T1 audit T2 compute T3 formal
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To evaluate the probability of a gravitational-wave candidate originating from noise, GstLAL collects noise statistics from the data it analyzes. Gravitational-wave signals of astrophysical origin get added to the noise statistics, harming the sensitivity of the search. We present the Background Filter, a novel tool to prevent this by removing noise statistics that were collected from gravitational-wave candidates. To demonstrate its efficacy, we analyze one week of LIGO and Virgo O3 data, and show that it improves the sensitivity of the analysis by 20-40% in the high mass region, in the presence of 868 simulated gravitational-wave signals. With the upcoming fourth observing run of LIGO, Virgo, and KAGRA expected to yield a high rate of gravitational-wave detections, we expect the Background Filter to be an important tool for increasing the sensitivity of a GstLAL analysis.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Method to get Better Sky Maps in a GstLAL Low-Latency Analysis

    astro-ph.IM 2026-06 unverdicted novelty 6.0 of 10

    A targeted hierarchical search refines GstLAL low-latency gravitational wave candidates to improve sky localization by 16.75% on average using O3 data and has been adopted for O4.

  2. GW250114: testing Hawking's area law and the Kerr nature of black holes

    gr-qc 2025-09 accept novelty 5.0 of 10

    GW250114 data confirm the remnant black hole ringdown frequencies lie within 30% of Kerr predictions and that the final horizon area is larger than the sum of the progenitors' areas to high credibility.

  3. New Methods for Offline GstLAL Analyses

    gr-qc 2025-06 conditional novelty 5.0 of 10

    New GstLAL offline methods reuse online matched-filtering products, merge with a heavy black hole search, revise the likelihood ratio and background estimation, and deliver a 50-100% sensitivity gain for high-mass eve...

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