$\textit{Kilonova Seekers}$: the GOTO project for real-time citizen science in time-domain astrophysics

TL;DR

The paper presents the Kilonova Seekers citizen science project, which leverages volunteers to classify transient astronomical phenomena in real-time, producing valuable training data and discoveries to enhance machine learning classifiers in time-domain astrophysics.

Contribution

It introduces a scalable citizen science platform for real-time classification of astronomical transients, generating a large training set and insights into volunteer performance.

Findings

20 discoveries of transient phenomena

Generated a training set of 17,682 detections

Measured volunteer performance and biases

Abstract

Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratised, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery -- with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the $\textit{Kilonova Seekers}$ citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. $\textit{Kilonova Seekers}$ launched in July 2023 and received over 600,000 classifications from approximately 2,000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a `gold-standard' training set of 17,682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development.