Shock_cooling_curve: A Python-based Package for Extensive and Efficient Modeling of Shock Cooling Emission in Supernovae

Abstract

The light-curve evolution of a supernova contains information on the exploding star. Early-time photometry of a variety of explosive transients, including Calcium-rich transients and type IIb/Ibc and IIP supernovae shows evidence for an early light curve peak as a result of the explosion’s shock wave passing through extended material (i.e., shock cooling emission (SCE)). Analytic modeling of the SCE allows us to estimate progenitor properties such as the radius and mass of extended material (e.g., the stellar envelope) as well as the shock velocity. In this work, we present a Python-based open-source code that implements four analytic models originally developed in Piro, Piro et al. and Sapir & Waxman applied to photometric data to obtain progenitor parameter properties via different modeling techniques (including nonlinear optimization, Markov Chain Monte Carlo sampling). Our software is easily extendable to other analytic models for SCE and different methods of parameter estimation.