Transient Classification in LIGO data using Difference Boosting Neural Network

TL;DR

This paper introduces a hybrid machine learning approach combining supervised and unsupervised techniques for classifying short-duration transients in LIGO gravitational wave data, improving detector characterization and signal detection.

Contribution

It presents a novel hybrid classification method using wavelet features and entropy, effective with minimal training data, for identifying transient noise and signals in gravitational wave data.

Findings

High accuracy on simulated transient classes

Successful classification of hardware injections

Effective in minimal training scenarios

Abstract

Detection and classification of transients in data from gravitational wave detectors are crucial for efficient searches for true astrophysical events and identification of noise sources. We present a hybrid method for classification of short duration transients seen in gravitational wave data using both supervised and unsupervised machine learning techniques. To train the classifiers we use the relative wavelet energy and the corresponding entropy obtained by applying one-dimensional wavelet decomposition on the data. The prediction accuracy of the trained classifier on 9 simulated classes of gravitational wave transients and also LIGO's sixth science run hardware injections are reported. Targeted searches for a couple of known classes of non-astrophysical signals in the first observational run of Advanced LIGO data are also presented. The ability to accurately identify transient classes using minimal training samples makes the proposed method a useful tool for LIGO detector characterization as well as searches for short duration gravitational wave signals.