DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data

TL;DR

This paper introduces a deep discriminative embedding method for clustering LIGO noise data, leveraging transfer learning from image classification to improve noise characterization and class discovery.

Contribution

It proposes a novel deep embedding approach that transfers knowledge from image domains to gravitational wave data for improved clustering and noise classification.

Findings

Effective in clustering LIGO noise data

Enables discovery of new noise classes

Improves understanding of noise sources

Abstract

In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks. The trained embedding function transfers knowledge from the domain of a labeled set of morphologically-distinct images, known as classes, to a new domain within which new classes can potentially be isolated and identified. Our target application in this paper is the Gravity Spy Project, which is an effort to characterize transient, non-Gaussian noise present in data from the Advanced Laser Interferometer Gravitational-wave Observatory, or LIGO. Accumulating large, labeled sets of noise features and identifying of new classes of noise lead to a better understanding of their origin, which makes their removal from the data and/or detectors possible.