甚高能伽马射线暴余辉的神经网络仿真与贝叶斯推断拟合
Very High-Energy GRB Afterglow Fits with Neural-Network Emulation and Bayesian inference
- Instituto de Radioastronomía y Astrofísica, Universidad Nacional Autónoma de México（墨西哥国立自治大学无线电天文学与天体物理研究所）
- Escuela Nacional de Estudios Superiores Unidad Morelia, Universidad Nacional Autónoma de México（墨西哥国立自治大学莫雷利亚高级研究国家学院）
- Astrophysics Research Center of the Open university (ARCO), The Open University of Israel（以色列开放大学天体物理学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用人工神经网络仿真单区SSC辐射代码，结合贝叶斯MCMC推断，高效拟合GRB 190114C的甚高能余辉，发现高能喷流参数，并指出ISM环境与观测不符，倾向径向分层介质。
AI中文摘要：
伽马射线暴（GRB）中的甚高能（$E>100$ GeV）余辉辐射可以用同步自康普顿（SSC）机制解释。精确建模这种辐射需要完全数值化的代码，在考虑Klein-Nishina抑制的同时求解时变耦合的粒子-光子动力学方程。然而，其巨大的计算成本使得它们不适用于传统参数推断技术，如马尔可夫链蒙特卡洛（MCMC）采样。在本工作中，我们提出了一种基于人工神经网络（ANN）的高效代理模型，用于模拟单区动力学SSC辐射代码。为训练我们的ANN，我们使用了一个多维模型参数网格，该网格包含由在恒定密度星际介质（ISM）中传播的无限薄球面爆炸波产生的100,800个光谱样本。ANN能很好地再现数值光谱，在整个频率域内中位相对误差通常低于2%。利用这一代理框架，我们通过MCMC进行贝叶斯参数后验估计，并拟合了GRB 190114C在不同历元从X射线到TeV $\gamma$-射线的宽带GRB余辉观测。我们发现一个各向同性等效动能$E_{\rm k,iso}\simeq7.2\times10^{54}$ erg的高能喷流，以体洛伦兹因子$\Gamma\gtrsim500$在密度$n\simeq0.1\\\\,{\rm cm^{-3}}$的ISM中运动。我们的模型光变曲线在长时间尺度上与观测的偏差表明，ISM环境是不一致的，而径向分层的外部介质，正如\citet{Aguilar-Ruiz+26}所指出的，是更可取的。
英文摘要：
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.
