arXiv 2409.056822024-09astro-ph.CO

ForestFlow：从线性到非线性尺度预测莱曼-α森林聚类

ForestFlow: predicting the Lyman-$α$ forest clustering from linear to nonlinear scales

J. Chaves-Montero, L. Cabayol-Garcia, M. Lokken, A. Font-Ribera, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferrar… 展开作者

J. Chaves-Montero, L. Cabayol-Garcia, M. Lokken, A. Font-Ribera, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, R. Kehoe, D. Kirkby, A. Kremin, A. Lambert, M. Landriau, M. Manera, P. Martini, R. Miquel, A. Muñoz-Gutiérrez, G. Niz, I. Pérez-Ràfols, G. Rossi, E. Sanchez, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver

备注：: 18 pages, 12 figures. Accepted in A&A
发表信息：: A&A 694, A187 (2025)
DOI：: 10.1051/0004-6361/202452039

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AI总结：

本研究提出ForestFlow框架，通过条件归一化流预测莱曼-α森林的线性偏置和小尺度功率谱偏差参数，结合玻尔兹曼求解器实现跨尺度一致预测，在大质量中微子等扩展模型中表现优异，将助力DESI巡天的全尺度分析。

摘要：

在大尺度上，莱曼-α森林为宇宙膨胀历史提供了见解，而在小尺度上，它对增长历史、暗物质性质和中微子质量总和施加了严格约束。本研究引入ForestFlow，这是一种新型框架，旨在弥合大尺度与小尺度分析之间的差距，这两种分析传统上依赖于不同的建模方法。利用条件归一化流，ForestFlow预测两个莱曼-α线性偏置（b_δ和b_η）以及六个参数，这些参数描述三维通量功率谱（P_3D）与线性理论在小尺度上的偏差，且偏差是宇宙学和星系际介质物理的函数。然后将这些参数与玻尔兹曼求解器结合，从任意大尺度到非线性区域，对P_3D及从中导出的任何其他统计量进行一致预测。基于涵盖红移z=2至4.5的30组固定配对宇宙学流体动力学模拟训练后，ForestFlow在描述P_3D和一维通量功率谱（P_1D）时，分别在从线性尺度到k=5 Mpc⁻¹和k_∥=4 Mpc⁻¹的范围内达到3%和1.5%的精度。得益于其条件参数化，ForestFlow在电离历史和两个ΛCDM模型扩展（大质量中微子和曲率）方面表现出相似性能，尽管这些内容均未包含在训练集中。该框架将为DESI巡天的莱曼-α森林测量提供全尺度宇宙学分析。

英文摘要：

On large scales, the Lyman-$α$ forest provides insights into the expansion history of the Universe, while on small scales, it imposes strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses. This work introduces ForestFlow, a novel framework that bridges the gap between large- and small-scale analyses, which have traditionally relied on distinct modeling approaches. Using conditional normalizing flows, ForestFlow predicts the two Lyman-$α$ linear biases ($b_δ$ and $b_η$) and six parameters describing small-scale deviations of the three-dimensional flux power spectrum ($P_\mathrm{3D}$) from linear theory as a function of cosmology and intergalactic medium physics. These are then combined with a Boltzmann solver to make consistent predictions, from arbitrarily large scales down to the nonlinear regime, for $P_\mathrm{3D}$ and any other statistics derived from it. Trained on a suite of 30 fixed-and-paired cosmological hydrodynamical simulations spanning redshifts from $z=2$ to 4.5, ForestFlow achieves 3 and 1.5\% precision in describing $P_\mathrm{3D}$ and the one-dimensional flux power spectrum ($P_\mathrm{1D}$) from linear scales to $k=5\,\mathrm{Mpc}^{-1}$ and $k_\parallel=4\,\mathrm{Mpc}^{-1}$, respectively. Thanks to its conditional parameterization, ForestFlow shows similar performance for ionization histories and two $Λ$CDM model extensions $\unicode{x2013}$ massive neutrinos and curvature $\unicode{x2013}$ even though none of these are included in the training set. This framework will enable full-scale cosmological analyses of Lyman-$α$ forest measurements from the DESI survey.

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英文摘要：

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