In:
The Astrophysical Journal, American Astronomical Society, Vol. 949, No. 2 ( 2023-06-01), p. 114-
Kurzfassung:
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 .
Materialart:
Online-Ressource
ISSN:
0004-637X
,
1538-4357
DOI:
10.3847/1538-4357/acc536
Sprache:
Unbekannt
Verlag:
American Astronomical Society
Publikationsdatum:
2023
ZDB Id:
2207648-7
ZDB Id:
1473835-1
SSG:
16,12
