发表机构
Department of Physics, Harvard University（哈佛大学物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种基于流匹配的生成模型，在CAMELS50模拟套件上训练以重建三维气体密度场，可估计FRB的DM，能泛化至更大体积，为从FRB等探针约束反馈模型提供推断路径。
AI 中文摘要
星系与大尺度弥散气体之间的复杂关系强烈依赖于天体物理反馈和宇宙学。快速射电暴（FRB）的色散量（DM）是宇宙重子密度的柱积分，且由星系际介质主导，而传统晕模型的预设未必能捕捉到这一点。我们引入一种生成模型，从包含红移空间畸变的星系巡天数据中重建三维气体密度场。该概率模型在CAMELS50模拟套件（IllustrisTNG）上训练，学习预测气体场的后验分布，条件包括恒星质量加权星系密度、宇宙学参数、星系样本的最小恒星质量阈值，以及作为重子反馈度量的重子抑制功率谱。我们的方法利用三维气体重建来估计每个视线方向的FRB DM及其后验分布。我们在测试框上验证该模型，结果显示在单个视线方向和整体DM分布上，预测值与真实值一致。该模型可泛化至体积约为训练框4³倍的更大框，能够覆盖宇宙学FRB视线方向所跨越的体积。模型的三维气体密度预测随输入功率谱和反馈场景变化，为从FRB及X射线、SZ效应、弱引力透镜等其他探针约束反馈模型提供了一条推断路径。
英文摘要
The complex relationship between galaxies and diffuse gas at large scales depends strongly on astrophysical feedback and cosmology. The dispersion measure (DM) of Fast Radio Bursts (FRBs) is a column of cosmic baryon density and is dominated by the intergalactic medium, which is not necessarily captured by traditional halo model prescriptions. We introduce a generative model to reconstruct 3D gas density fields from galaxy surveys, including redshift space distortions. The probabilistic model is trained on the CAMELS50 simulation suite (IllustrisTNG) and learns to predict the posterior distribution of gas fields conditioned on stellar mass-weighted galaxy density, cosmological parameters, the minimum stellar mass threshold for the galaxy sample, and the baryon suppression power spectrum as a measure of baryonic feedback. Our method uses the 3D gas reconstruction to estimate per-sightline FRB DMs and their posteriors. We validate the model on test boxes by showing agreement between predicted and true DM across individual lines of sight and the overall DM distribution. The model generalizes to a box size $\sim$\,4$^3$ times larger in volume than the training boxes and can cover the volumes spanned by cosmological FRB sightlines. The model's 3D gas density predictions vary with input power spectrum and feedback scenarios, offering an inference path to constrain feedback models from FRBs and other probes such as X-rays, the SZ effect, and weak lensing.
