标题： 捕捉小尺度再电离物理：SCRIPT的光子吸收模型 显示英文标题

标题： Capturing Small-Scale Reionization Physics: A Sub-Grid Model for Photon Sinks with SCRIPT

摘要： 再电离时期是宇宙历史中的一个重大相变阶段，在此期间，第一批发光天体电离了星系际介质（IGM）。然而，控制电离光子汇的小尺度物理过程——特别是复合、光子传播和自屏蔽区域之间的相互作用——仍然了解甚少。准确建模这些过程需要一个自洽的框架，将电离发射率、团块因子、平均自由程和光致电离率联系起来。在本工作中，我们通过引入一个自洽的亚网格模型扩展了光子守恒的半数值框架SCRIPT，该模型动态地将这些量与底层密度场连接起来，从而更真实地处理不均匀的复合和光子汇。我们将模型与一系列观测约束进行了验证，包括来自哈勃太空望远镜（HST）和詹姆斯·韦布太空望远镜（JWST）的紫外光度函数、普朗克卫星的宇宙微波背景（CMB）光学深度，以及类星体光谱中Lyman-$\alpha$丛林测量的IGM温度、光致电离率和平均自由程。我们的基准模型还成功再现了Lyman-$\alpha$透明度波动，进一步证明了其捕捉再电离过程中大尺度不均匀性的能力。值得注意的是，我们展示了传统上独立的参数，如团块因子和平均自由程，存在强烈相关性，这对再电离的时间、形态和热演化具有重要意义。展望未来，我们将扩展该框架以包含基于机器学习的参数推断。随着即将进行的21厘米实验有望提供前所未有的洞察，SCRIPT为解释高红移观测和深化对宇宙最后一次重大相变的理解提供了强大的计算工具。 显示英文摘要

摘要： The epoch of reionization represents a major phase transition in cosmic history, during which the first luminous sources ionized the intergalactic medium (IGM). However, the small-scale physics governing ionizing photon sinks - particularly the interplay between recombinations, photon propagation, and self-shielded regions - remains poorly understood. Accurately modeling these processes requires a framework that self-consistently links ionizing emissivity, the clumping factor, mean free path, and photoionization rate. In this work, we extend the photon-conserving semi-numerical framework, SCRIPT, by introducing a self-consistent sub-grid model that dynamically connects these quantities to the underlying density field, enabling a more realistic treatment of inhomogeneous recombinations and photon sinks. We validate our model against a comprehensive set of observational constraints, including the UV luminosity function from HST and JWST, CMB optical depth from Planck, and Lyman-$\alpha$ forest measurements of the IGM temperature, photoionization rate, and mean free path. Our fiducial model also successfully reproduces Lyman-$\alpha$ opacity fluctuations, reinforcing its ability to capture large-scale inhomogeneities in the reionization process. Notably, we demonstrate that traditionally independent parameters, such as the clumping factor and mean free path, are strongly correlated, with implications for the timing, morphology, and thermal evolution of reionization. Looking ahead, we will extend this framework to include machine learning-based parameter inference. With upcoming 21cm experiments poised to provide unprecedented insights, SCRIPT offers a powerful computational tool for interpreting high-redshift observations and refining our understanding of the last major phase transition in the universe.