The Magnetar Model for Type I Superluminous Supernovae. I. Bayesian Analysis of the Full Multicolor Light-curve Sample with MOSFiT

Abstract

We use the new Modular Open Source Fitter for Transients (MOSFiT) to model 38 hydrogen-poor superluminous supernovae (SLSNe). We fit their multicolour light curves with a magnetar spin-down model and present the posterior distributions of magnetar and ejecta parameters. The colour evolution of all SLSNe can be well matched with a simple absorbed blackbody. We find the following medians (1σ ranges) for the key parameters: spin period 2.4 ms (1.2-4 ms); magnetic field 0.8 × 10 14 G (0.2-1.8 ×10 14 G); ejecta mass 4.8 M (2.2-12.9 M ); kinetic energy 3.9 × 10 51 erg (1.9-9.8 ×10 51 erg). This significantly narrows the parameter space compared to our uninformed priors, showing that although the magnetar model is flexible, the parameter space relevant to SLSNe is actually well constrained by existing data. The requirement that the instantaneous engine power is ∼ 10 44 erg at the light curve peak necessitates either a large rotational energy (P < 2 ms), or more commonly that the spin-down and diffusion timescales be well-matched. We find no evidence for separate populations of fastand slow-declining SLSNe, which instead form a continuum both in light curve widths and inferred parameters. Variations in the spectra are well explained through differences in spin-down power and photospheric radii at maximum-light. We find no correlations between any model parameters and the properties of SLSN host galaxies. Comparing our posteriors to stellar evolution models, we show that SLSNe require rapidly rotating (fastest 10%) massive stars ( 20 M ), and that this is consistent with the observed SLSN rate. High mass, low metallicity, and likely binary interaction all serve to maintain rapid rotation essential for magnetar formation. By reproducing the full set of SLSN light curves, our posteriors can be used to inform photometric searches for SLSNe in future survey data.

Hydrogen-poor Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey

“…Although PS1-12bqf has a lower luminosity than most H-poor SLSNe, the fact that it can be well fit with a magnetar should not be surprising: magnetar models have a large parameter space and can naturally produce light curves with a range of luminosities. We note that the values we derive for PS1-12bqf are within the distribution of parameters found by Nicholl et al (2017b) and similar to what they derive for the SLSNe PTF10hgi and LSQ14mo. Our code is not set up to do a full parameter exploration and calculate confidence intervals; we refer the reader to recent Markov Chain Monte Carlo (MCMC) efforts to model H-poor SLSNe for typical parameter ranges (Guillochon et al 2017;Liu et al 2017a;Nicholl et al 2017b).…”

“…We note that the values we derive for PS1-12bqf are within the distribution of parameters found by Nicholl et al (2017b) and similar to what they derive for the SLSNe PTF10hgi and LSQ14mo. Our code is not set up to do a full parameter exploration and calculate confidence intervals; we refer the reader to recent Markov Chain Monte Carlo (MCMC) efforts to model H-poor SLSNe for typical parameter ranges (Guillochon et al 2017;Liu et al 2017a;Nicholl et al 2017b). Such MCMC efforts are also better suited to explore degeneracies and covariances between the different parameters.…”

“…This could also be contributing to the spread in observed colors, although the host galaxies of most of the SLSNe in this sample were studied in Lunnan et al (2014) and found to have little inferred dust extinction; the same result is found in other studies and appears to be true for SLSN host galaxies in general (e.g., Leloudas et al 2015;Perley et al 2016). Therefore, we do not generally expect a large contribution from the host galaxies; this is also supported by the low average extinction found in the modeling by Nicholl et al (2017b). In individual cases, host galaxy reddening may still be important, however.…”

“…Given the varying coverage and quality of our light curves, we do not attempt to model the full set or make inferences about the underlying distributions of parameters assuming a magnetar model. Indeed, the objects from our sample that were previously published already have magnetar fits available in the literature (Chomiuk et al 2011;Lunnan et al 2013Lunnan et al , 2016McCrum et al 2014McCrum et al , 2015Nicholl et al 2017b). Our sample contains two previously unpublished objects, PS1-11aib and PS1-12bqf, with good coverage both on the rise and decline, however, and we explore magnetar model fits to these light curves here.…”

“…Although this is reminiscent of the division by Gal-Yam (2012) into "SLSN-I" and "SLSN-R," we do not include any light curve information or intend to imply anything regarding the power source by making this distinction; we simply wish to include all kinds of H-poor SLSNe. Indeed, there are examples of objects (e.g., PS1-11ap and PTF12dam; Nicholl et al 2013;McCrum et al 2014) that resembled SN 2005ap near peak but developed features similar to SN 2007bi on their decline; it has been suggested that the differences mainly arise due to temperature effects (Nicholl et al 2017b). Here, we use the spectrum taken closest to peak light for classification, to make the selection as uniform as possible.…”