Application of Common Spatial Patterns in Gravitational Waves Detection

TL;DR

This paper adapts the Common Spatial Patterns algorithm, traditionally used in brain signal analysis, to detect gravitational wave events with high accuracy, demonstrating its effectiveness on real GW data.

Contribution

It introduces a novel application of CSP to gravitational wave detection, combining it with signal processing and logistic regression for improved event identification.

Findings

Detected 76 out of 82 confident GW events with 93.72% accuracy

Achieved high classification performance using cross-validation

Successfully adapted CSP for multi-channel GW strain data

Abstract

Common Spatial Patterns (CSP) is a feature extraction algorithm widely used in Brain-Computer Interface (BCI) Systems for detecting Event-Related Potentials (ERPs) in multi-channel magneto/electroencephalography (MEG/EEG) time series data. In this article, we develop and apply a CSP algorithm to the problem of identifying whether a given epoch of multi-detector Gravitational Wave (GW) strains contains coalescenses. Paired with Signal Processing techniques and a Logistic Regression classifier, we find that our pipeline is correctly able to detect 76 out of 82 confident events from Gravitational Wave Transient Catalog, using H1 and L1 strains, with a classification score of $93.72 \pm 0.04\%$ using $10 \times 5$ cross validation. The false negative events were: GW170817-v3, GW191219 163120-v1, GW200115 042309-v2, GW200210 092254-v1, GW200220 061928-v1, and GW200322 091133-v1.