Superluminous Supernovae in LSST: Rates, Detection Metrics, and Light-curve Modeling

V. Ashley Villar, Matt Nicholl, Edo Berger

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


We explore and demonstrate the capabilities of the upcoming Large Synoptic Survey Telescope (LSST) to study Type I superluminous supernovae (SLSNe). We fit the light curves of 58 known SLSNe at z ≈ 0.1-1.6, using a magnetar spin-down model. We use the posterior distributions of the magnetar and ejecta parameters to generate synthetic SLSN light curves, and we inject those into the LSST Operations Simulator to generate ugrizy light curves. We define metrics to quantify the detectability and utility of the light curve. We combine the metric efficiencies with the SLSN volumetric rate to estimate the discovery rate of LSST and find that ≈104 SLSNe per year with >10 data points will be discovered in the Wide-Fast-Deep (WFD) survey at z ≲ 3.0, while only ≈15 SLSNe per year will be discovered in each Deep Drilling Field at z ≲ 4.0. To evaluate the information content in the LSST data, we refit representative output light curves. We find that we can recover physical parameters to within 30% of their true values from ≈18% of WFD light curves. Light curves with measurements of both the rise and decline in gri-bands, and those with at least 50 observations in all bands combined, are most information rich. WFD survey strategies, which increase cadence in these bands and minimize seasonal gaps, will maximize the number of scientifically useful SLSNe. Finally, although the Deep Drilling Fields will provide more densely sampled light curves, we expect only ≈50 SLSNe with recoverable parameters in each field in the decade-long survey.

Original languageEnglish (US)
Article number166
JournalAstrophysical Journal
Issue number2
StatePublished - Dec 20 2018

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


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