Killestein, T. L. and Kelsey, L. and Wickens, E. and Nuttall, L. and Lyman, J. and Krawczyk, C. and Ackley, K. and Dyer, M. J. and Jiménez-Ibarra, F. and Ulaczyk, K. and O'Neill, D. and Kumar, A. and Steeghs, D. and Galloway, D. K. and Dhillon, V. S. and O'Brien, P. and Ramsay, G. and Noysena, K. and Kotak, R. and Breton, R. P. and Pallé, E. and Pollacco, D. and Awiphan, S. and Belkin, S. and Chote, P. and Clark, P. and Coppejans, D. and Duffy, C. and Eyles-Ferris, R. and Godson, B. and Gompertz, B. and Graur, O. and Irawati, P. and Jarvis, D. and Julakanti, Y. and Kennedy, M. R. and Kuncarayakti, H. and Levan, A. and Littlefair, S. and Magee, M. and Mandhai, S. and Sánchez, D. Mata and Mattila, S. and McCormac, J. and Mullaney, J. and Munday, J. and Patel, M. and Pursiainen, M. and Rana, J. and Sawangwit, U. and Stanway, E. (2024) Kilonova Seekers : the GOTO project for real-time citizen science in time-domain astrophysics. Monthly Notices of the Royal Astronomical Society, 533 (2). pp. 2113-2132. ISSN 0035-8711
Full text not available from this repository.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.