Very High-Energy GRB Afterglow Fits with Neural-Network Emulation and Bayesian inference

Abstract

The very high-energy ($E>100$ GeV) afterglow emission in gamma-ray bursts (GRBs) can be explained by synchrotron self-Compton (SSC). Modeling this emission accurately requires fully numerical codes that solve the time-dependent coupled particle--photon kinetic equations while accounting for Klein--Nishina suppression. However, their substantial computational cost makes them impractical for traditional parameter inference techniques such as Markov Chain Monte Carlo (MCMC) sampling. In this work, we present a highly efficient surrogate model based on artificial neural networks (ANNs) that emulates a one-zone kinetic SSC radiation code. To train our ANN, we use a multi-dimensional model parameter grid that constitutes a sample of 100,800 spectra produced by an infinitely thin spherical blast wave propagating inside a constant density interstellar medium (ISM). The ANN reproduces the numerical spectra well, with the median relative error generally below 2 per cent across the full frequency domain. Using this surrogate framework, we perform Bayesian parameter posterior estimation via MCMC and fit the broadband GRB afterglow observations of GRB 190114C from X-rays to TeV $\gamma$-rays at different epochs. We find an energetic jet with isotropic-equivalent kinetic energy of $E_{\mathrm{k},\mathrm{iso}}\simeq 7.2\times 10^{54}$ erg moving with a coasting bulk Lorentz factor of $\Gamma \gtrsim 500$ into an ISM with density $n\simeq 0.1\mathrm{c}{\mathrm{m}}^{-3}$. The deviation of our model light curve from observations on long timescales suggests that an ISM environment is inconsistent and a radially stratified external medium, as pointed out by \citet{Aguilar-Ruiz+26}, is preferred instead.

arXiv commentSubmitted to MNRAS