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

• Main Authors: Shah, Neev, Knee, Alan M, McIver, Jess, Stenning, David
• Format: Journal Article
• Language: English
• Published: 23-06-2023
• Subjects: Physics - General Relativity and Quantum Cosmology
• DOI: 10.48550/arxiv.2306.13787

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.