Radiative Transfer Modeling of Stripped-envelope Supernovae II: Neural Network Emulation of Light Curves

Abstract

We present the first neural-network emulator of stripped-envelope supernova (SESN) lightcurves, trained on a grid of 4499 light curves simulated with the radiative transfer (RT) code sedona. Using this emulator, we show that m_ni, m_ejecta, the ejecta velocity profile, and the degree of Ni-56 mixing can all be inferred from multiband lightcurves. We find that the degeneracy between ejecta mass and ejecta velocity is substantially weaker with this emulator than in traditional semianalytical models. The emulator is able to independently constrain the influence of ejecta mass and of ejecta velocity on the resulting lightcurve, rather than making them degenerate by design as traditional semianalytical models do. We additionally show that this inference is significantly more accurate than that done by the classical Arnett model for both simulated ZTF-like and LSST-like lightcurves. Finally, we present lightcurves fits to three well-studied SESNe: SN~1994I, SN~2007gr, and iPTF13bvn, constraining their m_ni, m_ejecta, and Ni-56 mixing.