{
  "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 $E_{\\rm k,iso}\\simeq7.2\\times10^{54}$ erg moving with a coasting bulk Lorentz factor of $Γ\\gtrsim500$ into an ISM with density $n\\simeq0.1\\,{\\rm cm^{-3}}$. 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38100",
  "author_total": 4,
  "authors": [
    {
      "name": "Edilberto Aguilar-Ruiz"
    },
    {
      "name": "Jessica Saldívar Talavera"
    },
    {
      "name": "Ramandeep Gill"
    },
    {
      "name": "Sundar Srinivasan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cm7kfbbpx6trr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38100",
  "fulltext_url": null,
  "id": "cm7kfbbpx6trr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38100",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.HE"
    ],
    [
      "openalex",
      "3103|Gamma-ray bursts and supernovae"
    ]
  ],
  "openalex": "W7214950405",
  "pdf_url": "https://arxiv.org/pdf/2609.38100",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cm7kfbbpx6trr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.38100",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38100"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    }
  ],
  "title": "Very High-Energy GRB Afterglow Fits with Neural-Network Emulation and Bayesian inference",
  "title_text": "Very High-Energy GRB Afterglow Fits with Neural-Network Emulation and Bayesian inference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/cm7kfbbpx6trr",
  "venue": "arXiv",
  "versions": []
}