Surrogate models for lightcurves and photosphere properties of Type II supernovae

TL;DR

This paper introduces fast surrogate models trained on simulations to accurately predict lightcurves and properties of Type II supernovae, enabling efficient inference of progenitor characteristics from observational data.

Contribution

The authors develop and validate a surrogate modeling approach that significantly speeds up predictions of supernova lightcurves and properties across a broad parameter space, improving inference accuracy.

Findings

Surrogate models predict supernova lightcurves within ~30ms.

Progenitor and nickel masses can be measured with ~9% and ~25% precision.

Application to real supernovae yields consistent progenitor mass estimates.

Abstract

Inferences on the properties Type II supernovae (SNe) can provide significant insights into the lives and deaths of the astrophysical population of massive stars and potentially provide measurements of luminosity distance, independent of the distance ladder. Here, we introduce surrogate models for the photospheric properties and lightcurves of Type II SNe trained on a large grid of simulations from the radiation hydrodynamics code, {\sc stella}. The trained model can accurately and efficiently ($\sim 30$ms) predict the lightcurves and properties of Type II SNe within a large parameter space of progenitor ($10-18 M_{\odot}$ at ZAMS) and nickel masses ($0.001-0.3M_{\odot}$), progenitor mass-loss rate ($10^{-5}-10^{-1}~M_{\odot}$yr$^{-1}$), CSM radius ($1-10\times10^{14}$cm), and SN explosion energies ($0.5-5 \times 10^{51}$erg). We validate this model through inference on lightcurves and photosphere properties drawn directly from the original {\sc stella} simulations not included in training. In particular, for a synthetic Type II SNe observed within the 10-year LSST survey, we find we can measure the progenitor and nickel masses with $\approx 9\%$ and $\approx 25\%$ precision, respectively, when fitting the photometric data while accounting for the uncertainty in the surrogate model itself. Meanwhile, from real observations of SN~2004et, SN~2012aw, and SN~2017gmr we infer a progenitor ZAMS mass of $12.15_{-1.06}^{+1.03} M_{\odot}$, $10.61_{-0.32}^{+0.37} M_{\odot}$, $10.4 \pm 0.3 M_{\odot}$, respectively. We discuss systematic uncertainties from our surrogate modelling approach and likelihood approaches to account for these uncertainties. We further discuss future extensions to the model to enable stronger constraints on properties of Type II SNe and their progenitors, and applications of our surrogate modelling approach to other transients.