Superluminous supernovae in LSST: rates, detection metrics, and light-curve modeling

We explore and demonstrate the capabilities of the upcoming Large Synoptic Survey Telescope (LSST) to studyType I superluminous supernovae (SLSNe). We fit the light curves of 58 known SLSNe at z ≈ 0.1–1.6, using amagnetar spin-down model. We use the posterior distributions of the magnetar and ejecta parameters to generatesynthetic SLSN light curves, and we inject those into the LSST Operations Simulator to generate ugrizy lightcurves. We define metrics to quantify the detectability and utility of the light curve. We combine the metricefficiencies with the SLSN volumetric rate to estimate the discovery rate of LSST and find that ≈104 SLSNe peryear with >10 data points will be discovered in the Wide-Fast-Deep (WFD) survey at z  3.0, while only ≈15SLSNe per year will be discovered in each Deep Drilling Field at z  4.0. To evaluate the information content inthe LSST data, we refit representative output light curves. We find that we can recover physical parameters towithin 30% of their true values from ≈18% of WFD light curves. Light curves with measurements of both the riseand decline in gri-bands, and those with at least 50 observations in all bands combined, are most informationrich. WFD survey strategies, which increase cadence in these bands and minimize seasonal gaps, will maximizethe 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