The first two years of FLEET: an active search for superluminous supernovae

Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh, Matt Nicholl, Daichi Hiramatsu, V. Ashley Villar, Yao Yin

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3 Citations (Scopus)
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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(SLSNI) > 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
Original languageEnglish
Article number114
Number of pages7
JournalThe Astrophysical Journal
Volume949
Issue number2
DOIs
Publication statusPublished - 01 Jun 2023
Externally publishedYes

Keywords

  • Supernovae
  • Core-collapse supernovae
  • Surveys
  • 1668
  • 304
  • 1671
  • Astrophysics - High Energy Astrophysical Phenomena

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