Empirically estimating the distribution of the loudest candidate from a gravitational-wave search

2021/11/30 by R. Tenorio, Rodrigo Tenorio, Luana M. Modafferi +5
Earth and Planetary Sciences · Mathematics · Physics and Astronomy · #Algorithm #Computer science #Data mining #Gamma-ray bursts and supernovae #Gravitational wave #LIGO #Mathematics #Physics #Pulsars and Gravitational Waves Research #Python (programming language) #Seismic Imaging and Inversion Techniques #Statistic #Statistical hypothesis testing #Statistics #Test statistic #astro-ph.IM #gr-qc #physics.data-an

paper · pdf · doi:10.1103/physrevd.105.044029

published as Phys. Rev. D 105, 044029 (2022) · 24 pages, 23 figures, comments welcome. Package freely available in https://github.com/Rodrigo-Tenorio/distromax

openalex created_date 2021/12/06 · arxiv created 2022/02/14 · openalex publication_date 2022/02/14 · arxiv updated 2022/02/15 · openalex updated_date 2026/08/06

Abstract

Searches for gravitational-wave signals are often based on maximizing a detection statistic over a bank of waveform templates, covering a given parameter space with a variable level of correlation. Results are often evaluated using a noise-hypothesis test, where the background is characterized by the sampling distribution of the loudest template. In the context of continuous gravitational-wave searches, properly describing said distribution is an open problem: current approaches focus on a particular detection statistic and neglect template-bank correlations. We introduce a new approach using extreme value theory to describe the distribution of the loudest template's detection statistic in an arbitrary template bank. Our new proposal automatically generalizes to a wider class of detection statistics, including (but not limited to) line-robust statistics and transient continuous-wave signal hypotheses, and improves the estimation of the expected maximum detection statistic at a negligible computing cost. The performance of our proposal is demonstrated on simulated data as well as by applying it to different kinds of (transient) continuous-wave searches using O2 Advanced LIGO data. We release an accompanying python software package, distromax, implementing our new developments.

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