TransFit-CSM: A Fast, Physically Consistent Framework for Interaction-Powered Transients
Yu-Hao Zhang, Liang-Duan Liu, Ze-Xin Du, Guang-Lei Wu, Jing-Yao Li, Yun-Wei Yu
TL;DR
TransFit-CSM presents a fast, physically grounded framework that self-consistently couples thin-shell ejecta–CSM dynamics with time-dependent radiative diffusion from a moving, shock-tied heating boundary. By solving the coupled shell dynamics and diffusion equations, the model naturally produces diffusion-mediated light-curve peaks and avoids Arnett-like assumptions in optically thick CSM. Bayesian fits to SN 2006gy and SN 2010jl demonstrate how massive, extended CSM with eruptive pre-SN mass loss shapes the observables and permits interpretable posteriors for ejecta and CSM properties. This framework thus provides a practical bridge between analytic models and full radiation-hydrodynamic simulations, enabling population-level inferences for current and future time-domain surveys.
Abstract
We present TransFit-CSM, a fast and physically consistent framework for modeling interaction-powered transients. The method self-consistently couples the ejecta circumstellar medium (CSM) shock dynamics to radiative diffusion from a moving heating boundary tied to the shocks, so that both the photon escape path and the effective diffusion time evolve with radius and time. We solve the mass and momentum equations for the forward and reverse shocks together with the diffusion equation in the unshocked CSM. TransFit-CSM reproduces the canonical sequence of an early dark phase, a diffusion-mediated rise and peak, and a post-interaction cooling tail, and it clarifies why Arnett-like peak scalings break down in optically thick CSM. The framework is well suited for Bayesian inference and constrains physical parameters of the ejecta and CSM from bolometric or joint multi-band light curves. Applications to SN 2006gy and SN 2010jl yield accurate fits and physically interpretable posteriors, highlighting the dominant role of pre-supernova mass loss in shaping the observables. Because it is both computationally efficient and physically grounded, TransFit-CSM bridges simple analytic prescriptions and radiation-hydrodynamic simulations, enabling population-level inference for current and future time-domain surveys.
