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 γ-rays at different epochs. We find an energetic jet with isotropic-equivalent kinetic energy of Ek,iso≃7.2×1054 erg moving with a coasting bulk Lorentz factor of Γ≳500 into an ISM with density n≃0.1 cm−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.
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BibTeX
@misc{aguilarruiz2026very,
title = {{Very High-Energy GRB Afterglow Fits with Neural-Network Emulation and Bayesian inference}},
author = {Edilberto Aguilar-Ruiz and Jessica Saldívar Talavera and Ramandeep Gill and Sundar Srinivasan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.38100},
eprint = {2609.38100},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.38100},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Very High-Energy GRB Afterglow Fits with Neural-Network Emulation and Bayesian inference
AU - Edilberto Aguilar-Ruiz
AU - Jessica Saldívar Talavera
AU - Ramandeep Gill
AU - Sundar Srinivasan
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.38100
UR - https://arxiv.org/abs/2609.38100
AB - 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 γ-rays at different epochs. We find an energetic jet with isotropic-equivalent kinetic energy of E_k,iso≃7.2×10^54 erg moving with a coasting bulk Lorentz factor of Γ≳500 into an ISM with density n≃0.1 cm^−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.
ER -