Program: GN-2023B-Q-241

Superluminous supernovae (SLSNe) are an exotic class of core-collapse supernovae (SNe) that can be up to 100 times brighter than normal SNe. Despite their large luminosity, SLSNe are rare and therefore their nature is not well understood. Late-time spectroscopy can provide valuable insights into their progenitors and power sources, but to date only a small fraction of SLSNe have been spectroscopically observed several months past their peak. FLEET is a machine-learning classifier that can rapidly identify SLSNe in time-domain survey alert streams with a demonstrated $85\%$ success rate (a factor of 40 times better than random selection). FLEET can be used to identify about 25 SLSNe in the 2022B semester, for which we propose to obtain one spectrum per SLSN at about 100 days after peak with Gemini+GMOS. These data will allow us to: (i) explore the origin of light curve ``bumps'' that appear to be ubiquitous on these timescales, but which currently lack spectral information, (ii) search for late-time signatures of hydrogen and helium, only tentatively claimed in a few SLSNe, and (iii) study whether the newly identified class of sub-energetic SLSNe exhibit spectroscopic differences compared to the more energetic events. These critical clues will help us to decipher the progenitors and mechanism powering SLSNe.