A1 Refereed original research article in a scientific journal
Kilonova Seekers: the GOTO project for real-time citizen science in time-domain astrophysics
Authors: Killestein, T L; Kelsey, L; Wickens, E; Nuttall, L; Lyman, J; Krawczyk, C; Ackley, K; Dyer, M J; Jiménez-Ibarra, F; Ulaczyk, K; O’Neill, D; Kumar, A; Steeghs, D; Galloway, D K; Dhillon, V S; O’Brien, P; Ramsay, G; Noysena, K; Kotak, R; Breton, R P; Pallé, E; Pollacco, D; Awiphan, S; Belkin, S; Chote, P; Clark, P; Coppejans, D; Duffy, C; Eyles-Ferris, R; Godson, B; Gompertz, B; Graur, O; Irawati, P; Jarvis, D; Julakanti, Y; Kennedy, M R; Kuncarayakti, H; Levan, A; Littlefair, S; Magee, M; Mandhai, S; Mata Sánchez, D; Mattila, S; McCormac, J; Mullaney, J; Munday, J; Patel, M; Pursiainen, M; Rana, J; Sawangwit, U; Stanway, E; Starling, R; Warwick, B; Wiersema, K
Publisher: Oxford University Press
Publication year: 2024
Journal: Monthly Notices of the Royal Astronomical Society
Journal name in source: Monthly Notices of the Royal Astronomical Society
Volume: 533
Issue: 2
First page : 2113
Last page: 2132
ISSN: 0035-8711
eISSN: 1365-2966
DOI: https://doi.org/10.1093/mnras/stae1817
Web address : https://doi.org/10.1093/mnras/stae1817
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457724449
Preprint address: https://arxiv.org/abs/2406.02334
Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratized, 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 Kilonova Seekers citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. Kilonova Seekers launched in 2023 July and received over 600 000 classifications from approximately 2000 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.
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Funding information in the publication:
TLK acknowledges support via an Research Council of Finland grant (340613; P.I. R. Kotak), and from the UK Science and Technology Facilities Council (STFC, grant number ST/T506503/1). LK and LN thank the UKRI Future Leaders Fellowship for support through the grant MR/T01881X/1. EW thanks STFC for support through the grant ST/Y509486/1. JDL acknowledges support from a UK Research and Innovation Fellowship (MR/T020784/1). DMS acknowledges support by the Spanish Ministry of Science via the Plan de Generacion de conocimiento PID2020-120323GB-I00 and PID2021-124879NB-I00. SM acknowledges support from the Research Council of Finland project 350458. The Gravitational-wave Optical Transient Observer (GOTO) project acknowledges the support of the Monash-Warwick Alliance; University of Warwick; Monash University; University of Sheffield; University of Leicester; Armagh Observatory & Planetarium; the National Astronomical Research Institute of Thailand (NARIT); Instituto de Astrofísica de Canarias (IAC); University of Portsmouth; University of Turku. We acknowledge support from the Science and Technology Facilities Council (STFC, grant numbers ST/T007184/1, ST/T003103/1, ST/T000406/1, ST/X001121/1, and ST/Z000165/1).
