Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Kilonova Seekers
T2 - the GOTO project for real-time citizen science in time-domain astrophysics
AU - Killestein, T. L.
AU - Kelsey, L.
AU - Wickens, E.
AU - Nuttall, L.
AU - Lyman, J.
AU - Krawczyk, C.
AU - Ackley, K.
AU - Dyer, M. J.
AU - Jiménez-Ibarra, F.
AU - Ulaczyk, K.
AU - O'Neill, D.
AU - Kumar, A.
AU - Steeghs, D.
AU - Galloway, D. K.
AU - Dhillon, V. S.
AU - O'Brien, P.
AU - Ramsay, G.
AU - Noysena, K.
AU - Kotak, R.
AU - Breton, R. P.
AU - Pallé, E.
AU - Pollacco, D.
AU - Awiphan, S.
AU - Belkin, S.
AU - Chote, P.
AU - Clark, P.
AU - Coppejans, D.
AU - Duffy, C.
AU - Eyles-Ferris, R.
AU - Godson, B.
AU - Gompertz, B.
AU - Graur, O.
AU - Irawati, P.
AU - Jarvis, D.
AU - Julakanti, Y.
AU - Kennedy, M. R.
AU - Kuncarayakti, H.
AU - Levan, A.
AU - Littlefair, S.
AU - Magee, M.
AU - Mandhai, S.
AU - Sánchez, D. Mata
AU - Mattila, S.
AU - McCormac, J.
AU - Mullaney, J.
AU - Munday, J.
AU - Patel, M.
AU - Pursiainen, M.
AU - Rana, J.
AU - Sawangwit, U.
AU - Stanway, E.
PY - 2024/9/30
Y1 - 2024/9/30
N2 - 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.
AB - 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.
UR - https://arxiv.org/abs/2406.02334
U2 - 10.48550/ARXIV.2406.02334
DO - 10.48550/ARXIV.2406.02334
M3 - Journal article
VL - 533
SP - 2113
EP - 2132
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
SN - 0035-8711
IS - 2
ER -