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Title: Inferring the astrophysical population of gravitational wave sources in the presence of noise transients
ABSTRACT

The global network of interferometric gravitational wave (GW) observatories (LIGO, Virgo, KAGRA) has detected and characterized nearly 100 mergers of binary compact objects. However, many more real GWs are lurking sub-threshold, which need to be sifted from terrestrial-origin noise triggers (known as glitches). Because glitches are not due to astrophysical phenomena, inference on the glitch under the assumption it has an astrophysical source (e.g. binary black hole coalescence) results in source parameters that are inconsistent with what is known about the astrophysical population. In this work, we show how one can extract unbiased population constraints from a catalogue of both real GW events and glitch contaminants by performing Bayesian inference on their source populations simultaneously. In this paper, we assume glitches come from a specific class with a well-characterized effective population (blip glitches). We also calculate posteriors on the probability of each event in the catalogue belonging to the astrophysical or glitch class, and obtain posteriors on the number of astrophysical events in the catalogue, finding it to be consistent with the actual number of events included.

 
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Award ID(s):
2045740
NSF-PAR ID:
10427684
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
523
Issue:
4
ISSN:
0035-8711
Page Range / eLocation ID:
p. 5972-5984
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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