# Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

## TL;DR

This paper introduces a similarity learning approach in the Gravity Spy citizen-science project to help identify and classify rare, unknown gravitational-wave detector glitches, enhancing the detection of novel noise transients.

## Contribution

It presents a new method using similarity indices to enable citizen scientists to create large datasets of unknown glitches for improved machine learning classification.

## Key findings

- Similarity indices helped identify new glitch types in LIGO data.
- The approach facilitated the collection of large samples of rare transient events.
- Demonstrated potential to improve detection of unknown noise sources.

## Abstract

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program.