We recently submitted a new paper to the Astrophysical Journal which presents detailed simualtions of Type I superluminous supernovae obseved with the upcoming Large Synoptic Survey Telescope.
We fit the light curves of 58 known SLSNe at z~0.1-1.6, using an analytical magnetar spin-down model implemented in MOSFiT. We then use the posterior distributions of the magnetar and ejecta parameters to generate thousands of synthetic SLSN light curves, and we inject those into the OpSim to generate realistic ugrizy light curves.
Sample SLSN light curves
We define simple, measurable metrics to quantify the detectability and utility of the light curve, and to measure the efficiency of LSST in returning SLSN light curves satisfying these metrics.
Recovery efficiency as a function of redshift
We combine the metric efficiencies with the volumetric rate of SLSNe to estimate the overall discovery rate of LSST, and we find that ~10^4 SLSNe per year with >10 data points will be discovered in the WFD survey at z<3.0, while only ~15 SLSNe per year will be discovered in each DDF at z<4.0.
Number of discovered SLSNe as a function of redshift
To evaluate the information content in the LSST data, we refit representative output light curves with the same model that was used to generate them.
Sample SLSN light curves with best-fit magnetar models
We correlate our ability to recover magnetar and ejecta parameters with the simple light curve metrics to evaluate the most important metrics. 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 fifty observations in all bands combined, are most information rich, with ~30% of these light curves having recoverable physical parameters to ~30% accuracy. WFD survey strategies which increase cadence in these bands and minimize seasonal gaps will maximize the number of scientifically useful SLSN light curves. Finally, although the DDFs will provide more densely sampled light curves, we expect only ~50 SLSNe with recoverable parameters in each field in the decade-long survey.
Recovered parameter accuracy as a function function of number of observations
So what’s the bottom line? LSST is going to be a beast when it comes to discovering SLSNe, although only about 20% of the light curves will be useful on their own (assuming we know the redshift). The DFFs, in their current form, aren’t going to help us a lot compared to the WFD. If the WFD survey could (1) occasionally observe between seasons or (2) stack late-time SLSN observations, we could extend the light curves of SLSNe, which would help us extract the most science without additional followup.
Superluminous Supernovae in LSST: Rates, Detection Metrics, and Light-curve ModelingThe Astrophysical Journal, 2018