TransFit : An Efficient Framework for Transient Light-curve Fitting with Time-dependent Radiative Diffusion

作者：Liang-Duan Liu, Yu-Hao Zhang, Yun-Wei Yu, Ze-Xin Du, Jing-Yao Li, Guang-Lei Wu, Zi-Gao Dai · 发表于：The Astrophysical Journal · 年份：2025 · DOI：10.3847/1538-4357/adfed6 · 被引用次数：4 · 研究领域：Advanced Image Processing Techniques、Advanced Vision and Imaging、Advanced Optical Sensing Technologies

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 semianalytical 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 data sets. 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 LC 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 LCs. By combining physical realism with computational speed, TransFit provides a powerful tool for efficiently inverting LCs and extracting detailed physical insights from the vast data sets of current and future transient surveys.