The First Two Years of FLEET: an Active Search for Superluminous Supernovae

TL;DR

FLEET is a machine learning tool developed to identify superluminous supernovae in transient data streams, achieving high purity and improved completeness over its initial version, and is now publicly available for community use.

Contribution

This paper introduces FLEET 2.0, an improved machine learning algorithm for photometric classification of superluminous supernovae, trained on a larger dataset and demonstrating enhanced completeness.

Findings

FLEET 2.0 achieves approximately 40% completeness for SLSNe with high probability.

FLEET's purity remains around 80%, confirming its reliability.

The recovered SLSNe population is representative of the overall SLSN population.

Abstract

In November 2019 we began operating FLEET (Finding Luminous and Exotic Extragalactic Transients), a machine learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Using FLEET, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between November 2019 and January 2022. Based on our original algorithm, we anticipated that FLEET would achieve a purity of about 50\% for transients with a probability of being a SLSN, \pslsn$>0.5$; 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 $\approx 33$\%. Here, we present FLEET 2.0, an updated version of FLEET trained on 4,780 transients (almost 3 times more than in 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 $\approx 40$\% for transients with \pslsn$>0.5$. Additionally, we explore 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