Surrogate models for light curves and photosphere properties of Type II supernovae

Le résumé fourni par la source

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 light curves of Type II SNe trained on a large grid of simulations from the radiation hydrodynamics code, stella. The trained model can accurately and efficiently (~30 ms) predict the light curves and properties of Type II SNe within a large parameter space of progenitor (10–18 ${\rm M}_{\odot }$ at ZAMS) and nickel masses (0.001–0.3 ${\rm M}_{\odot }$), progenitor mass-loss rate (10–5–10–1 ${\rm M}_{\odot }$ yr$^{-1}$), circumstellar matter radius (1–10 × 1014 cm), and SN explosion energies (0.5–5 × 1051 erg). We validate this model through inference on light curves and photosphere properties drawn directly from the original stella simulations not included in training. In particular, for a synthetic Type II SNe observed within the 10-yr Legacy Survey of Space and Time survey, we find we can measure the progenitor and nickel masses with $\approx $9 per cent and $\approx $25 per cent 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 zero-age main sequence (ZAMS) mass of $12.15_{-1.06}^{+1.03}$, $10.61_{-0.32}^{+0.37}$, $10.4 \pm 0.3 \,{\rm M}_{\odot }$, 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.

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