Waves in a Forest: A Random Forest Classifier to Distinguish between Gravitational Waves and Detector Glitches

The LIGO-Virgo-KAGRA (LVK) network of gravitational-wave (GW) detectors have observed many tens of compact binary mergers to date. Transient, non-Gaussian noise excursions, known as "glitches", can impact signal detection in various ways. They can imitate true signals as well as reduce the...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Shah, Neev, Knee, Alan M, McIver, Jess, Stenning, David
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.06.2023
Subjects
Classifiers
Gravitational waves
Mathematical models
Parameters
Random noise
Signal detection
Waveforms
Online AccessGet full text

Cover

More Information
Summary:The LIGO-Virgo-KAGRA (LVK) network of gravitational-wave (GW) detectors have observed many tens of compact binary mergers to date. Transient, non-Gaussian noise excursions, known as "glitches", can impact signal detection in various ways. They can imitate true signals as well as reduce the confidence of real signals. In this work, we introduce a novel statistical tool to distinguish astrophysical signals from glitches, using their inferred source parameter posterior distributions as a feature set. By modelling both simulated GW signals and real detector glitches with a gravitational waveform model, we obtain a diverse set of posteriors which are used to train a random forest classifier. We show that random forests can identify differences in the posterior distributions for signals and glitches, aggregating these differences to tell apart signals from common glitch types with high accuracy of over 93%. We conclude with a discussion on the regions of parameter space where the classifier is prone to making misclassifications, and the different ways of implementing this tool into LVK analysis pipelines.
ISSN:2331-8422