The First Two Years of FLEET: An Active Search for Superluminous Supernovae
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Gomez, S.Berger, E.
Blanchard, P.K.
Hosseinzadeh, G.
Nicholl, M.

Hiramatsu, D.
Villar, V.A.
Yin, Y.
Affiliation
Steward Observatory, University of ArizonaIssue Date
2023-06-02
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Institute of PhysicsCitation
Sebastian Gomez et al 2023 ApJ 949 114Journal
Astrophysical JournalRights
© 2023. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
In 2019 November, we began operating Finding Luminous and Exotic Extragalactic Transients (FLEET), a machine-learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Through this observational campaign, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between 2019 November and 2022 January. Based on our original algorithm, we anticipated that FLEET would achieve a purity of about 50% for transients with a probability of being an SLSN, P(SLSN-I) > 0.5; the true on-sky purity we obtained is closer to 80%. Similarly, we anticipated FLEET could reach a completeness of about 30%, and we indeed measure an upper limit on the completeness of ≲33%. Here we present FLEET 2.0, an updated version of FLEET trained on 4780 transients (almost three times more than FLEET 1.0). FLEET 2.0 has a similar predicted purity to FLEET 1.0 but outperforms FLEET 1.0 in terms of completeness, which is now closer to ≈40% for transients with P(SLSN-I) > 0.5. Additionally, we explore the possible systematics that might arise from the use of FLEET for target selection. We find that the population of SLSNe recovered by FLEET is mostly indistinguishable from the overall SLSN population in terms of physical and most observational parameters. We provide FLEET as an open source package on GitHub: https://github.com/gmzsebastian/FLEET. © 2023. The Author(s). Published by the American Astronomical Society.Note
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0004-637XVersion
Final Published Versionae974a485f413a2113503eed53cd6c53
10.3847/1538-4357/acc536
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Except where otherwise noted, this item's license is described as © 2023. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.