2023/05/29 by Prathamesh Joshi, Joshi, Prathamesh, Leo Tsukada +3 · 2 citations
Earth and Planetary Sciences · Engineering · Mathematics · Physics and Astronomy · #Acoustics #Artificial intelligence #Astronomy #Computer science #Computer vision #Electronic engineering #Engineering #FOS: Physical sciences #Filter (signal processing) #Gamma-ray bursts and supernovae #General Relativity and Quantum Cosmology (gr-qc) #Geophysics and Gravity Measurements #Gravitational wave #LIGO #Mathematics #Noise (video) #Physics #Pulsars and Gravitational Waves Research #SIGNAL (programming language) #Sensitivity (control systems) #Statistics
paper · pdf · doi:10.48550/arxiv.2305.18233
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.