Detecting transient gravitational waves in non-Gaussian noise with partially redundant analysis methods

Abstract

There is a broad class of astrophysical sources that produce detectable, transient, gravitational waves. Some searches for transient gravitational waves are tailored to known features of these sources. Other searches make few assumptions about the sources. Typically events are observable with multiple search techniques. This work describes how to combine the results of searches that are not independent, treating each search as a classifier for a given event. This will be shown to improve the overall sensitivity to gravitational-wave events while directly addressing the problem of consistent interpretation of multiple trials.

11 pages, 5 figures

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Cited by in corpus (8)

• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• Detecting binary compact-object mergers with gravitational waves: Understanding and Improving the sensitivity of the PyCBC search
• Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
• An optimized PyCBC search for gravitational waves from intermediate-mass black hole mergers
• Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
• Sensitivity Comparison of Searches for Binary Black Hole Coalescences with Ground-based Gravitational-Wave Detectors
• A Unified $p_{\mathrm{astro}}$ for Gravitational Waves: Consistently Combining Information from Multiple Search Pipelines
• Beyond the Horizon Distance: LIGO-Virgo can Boost Gravitational Wave Detection Rates by Exploiting the Mass Distribution of Neutron Stars