FLEET: a redshift-agnostic machine learning pipeline to rapidly identify hydrogen-poor superluminous supernovae

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Over the past decade wide-field optical time-domain surveys have increased the discovery rate of transients to the point that ≲10% are being spectroscopically classified. Despite this, these surveys have enabled the discovery of new and rare types of transients, most notably the class of hydrogen-poor superluminous supernovae (SLSN-I), with about 150 events confirmed to date. Here we present a machine-learning classification algorithm targeted at rapid identification of a pure sample of SLSN-I to enable spectroscopic and multiwavelength follow-up. This algorithm is part of the Finding Luminous and Exotic Extragalactic Transients (FLEET) observational strategy. It utilizes both light-curve and contextual information, but without the need for a redshift, to assign each newly discovered transient a probability of being a SLSN-I. This classifier can achieve a maximum purity of about 85% (with 20% completeness) when observing a selection of SLSN-I candidates. Additionally, we present two alternative classifiers that use either redshifts or complete light curves and can achieve an even higher purity and completeness. At the current discovery rate, the FLEET algorithm can provide about 20 SLSN-I candidates per year for spectroscopic follow-up with 85% purity; with the Legacy Survey of Space and Time we anticipate this will rise to more than ∼103 events per year.

Original languageEnglish
Article number74
Number of pages12
JournalThe Astrophysical Journal
Issue number1
Publication statusPublished - 23 Nov 2020
Externally publishedYes


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


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