We present an expansion of FLEET, a machine-learning algorithm optimized to select transients that are most likely tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on both the light curves and host galaxy information of 4779 spectroscopically classified transients. We find that for transients with a probability of being a TDE,
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,
- Award ID(s):
- 2108531
- NSF-PAR ID:
- 10418475
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 949
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 114
- Size(s):
- ["Article No. 114"]
- Sponsoring Org:
- National Science Foundation
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