AI 中文总结
研究伽马射线暴瞬时辐射起源，采用机器学习框架，通过遗传算法优化内激波模型，依据三个GRB目录校准参数，再现关键观测特性，推导中央引擎活动约束，为解释GRB变异性和预测可探测群体提供物理框架。
AI 中文摘要
伽马射线暴（GRB）瞬时辐射的起源仍是一个悬而未决的问题。内激波（IS）模型是将相对论性抛射体动能转化为伽马射线的主要模型，但尚未根据观测到的GRB光变曲线（LC）对其参数进行充分校准以再现其多样性。我们采用机器学习框架，通过比较三个GRB目录（Swift/BAT、Fermi/GBM、CGRO/BATSE）的模拟和观测LC特性来优化IS模型。假设GRB形成率与红移有关，我们使用遗传算法基于六个独立指标最小化损失函数，这些指标捕获平均行为和统计分布。优化后的模型再现了几个关键观测特性。我们还推导了对中央引擎活动的约束：（i）发射壳的数量可用广义齐普夫分布很好地描述，类似于地震的古登堡-里希特定律，（ii）静止系壳发射时间遵循负指数分布，表明是具有恒定喷射概率的随机过程。这种校准后的IS模型为解释GRB变异性和预测未来任务可探测的GRB群体提供了一个基于物理的框架。
英文摘要
The origin of gamma-ray burst (GRB) prompt emission remains an open question. The internal shock (IS) model is a leading scenario for converting relativistic ejecta kinetic energy into gamma rays, but its parameters have not yet been fully calibrated against observed GRB light curves (LCs) to reproduce their diversity. We adopt a machine-learning framework to optimise the IS model by comparing simulated and observed LC properties from three GRB catalogues (Swift/BAT, Fermi/GBM, CGRO/BATSE). Assuming a redshift-dependent GRB formation rate, we employ a genetic algorithm to minimise a loss function based on six independent metrics capturing both average behaviours and statistical distributions. The optimised model reproduces several key observational properties, including the average post-peak temporal profile, autocorrelation function, and the distributions of duration, signal-to-noise ratio, number of peaks, peak flux, and fluence. We also derive constraints on the central engine activity: (i) the number of emitted shells is well described by a generalised Zipf distribution, analogous to the Gutenberg-Richter law for earthquakes, and (ii) the rest-frame shell-emission times follow a negative exponential distribution, indicating a stochastic process with a constant ejection probability. This calibrated IS model provides a physically grounded framework for interpreting GRB variability and predicting GRB populations detectable by future missions.
Comments11 pages, 8 figures, A&A accepted for publication
