English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The LIGO-Virgo-KAGRA (LVK) network of gravitational-wave (GW) detectors haveobserved many tens of compact binary mergers to date. Transient, non-Gaussiannoise excursions, known as "glitches", can impact signal detection in variousways. They can imitate true signals as well as reduce the confidence of realsignals. In this work, we introduce a novel statistical tool to distinguishastrophysical signals from glitches, using their inferred source parameterposterior distributions as a feature set. By modelling both simulated GWsignals and real detector glitches with a gravitational waveform model, weobtain a diverse set of posteriors which are used to train a random forestclassifier. We show that random forests can identify differences in theposterior distributions for signals and glitches, aggregating these differencesto 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 theclassifier is prone to making misclassifications, and the different ways ofimplementing this tool into LVK analysis pipelines.

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