TransFit: An Efficient Framework for Transient Light-Curve Fitting with Time-Dependent Radiative Diffusion

TL;DR

TransFit is a new framework that efficiently models transient light curves by solving a generalized energy conservation equation, capturing time-dependent diffusion and heating effects for large survey datasets.

Contribution

It introduces a physically realistic, computationally efficient model for transient light curves that accounts for time-dependent radiative diffusion and heating processes.

Findings

Accurately models peak luminosity and rise time of transients.

Captures transition from shock-cooling to radioactive heating.

Enables efficient analysis of large transient datasets.

Abstract

Modeling the light curves (LCs) of luminous astronomical transients, such as supernovae, is crucial for understanding their progenitor physics, particularly with the exponential growth of survey data. However, existing methods face limitations: efficient semi-analytical models (e.g., Arnett-like) employ significant physical simplifications (like time-invariant temperature profiles and simplified heating distributions), often compromising accuracy, especially for early-time LCs. Conversely, detailed numerical radiative transfer simulations, while accurate, are computationally prohibitive for large datasets. This paper introduces TransFit, a novel framework that numerically solves a generalized energy conservation equation, explicitly incorporating time-dependent radiative diffusion, continuous radioactive or central engine heating, and ejecta expansion dynamics. The model accurately captures the influence of key ejecta properties and diverse heating source characteristics on light curve morphology, including peak luminosity, rise time, and overall shape. Furthermore, TransFit provides self-consistent modeling of the transition from shock-cooling to $^{56}$Ni}-powered light curves. By combining physical realism with computational speed, TransFit provides a powerful tool for efficiently inverting LCs and extracting detailed physical insights from the vast datasets of current and future transient surveys.