Galaxies are biased tracers of the underlying cosmic web, which is dominated
by dark matter components that cannot be directly observed. The relationship
between dark matter density fields and galaxy distributions can be sensitive to
assumptions in cosmology and astrophysical processes embedded in the galaxy
formation models, that remain uncertain in many aspects. Based on
state-of-the-art galaxy formation simulation suites with varied cosmological
parameters and sub-grid astrophysics, we develop a diffusion generative model
to predict the unbiased posterior distribution of the underlying dark matter
fields from the given stellar mass fields, while being able to marginalize over
the uncertainties in cosmology and galaxy formation.

基于先进的星系形成模拟套件，利用扩散生成模型预测给定恒星质量场的潜在暗物质场的无偏后验分布，并能够消除在宇宙学和星系形成中的不确定性。

使用扩散模型进行有偏追踪器的暗物质场概率重建

Probabilistic reconstruction of Dark Matter fields from biased tracers  using diffusion models

We present Monte Carlo Physarum Machine: a computational model suitable for
reconstructing continuous transport networks from sparse 2D and 3D data. MCPM
is a probabilistic generalization of Jones's 2010 agent-based model for
simulating the growth of Physarum polycephalum slime mold. We compare MCPM to
Jones's work on theoretical grounds, and describe a task-specific variant
designed for reconstructing the large-scale distribution of gas and dark matter
in the Universe known as the Cosmic web. To analyze the new model, we first
explore MCPM's self-patterning behavior, showing a wide range of continuous
network-like morphologies -- called "polyphorms" -- that the model produces
from geometrically intuitive parameters. Applying MCPM to both simulated and
observational cosmological datasets, we then evaluate its ability to produce
consistent 3D density maps of the Cosmic web. Finally, we examine other
possible tasks where MCPM could be useful, along with several examples of
fitting to domain-specific data as proofs of concept.

提出了一种用于重建连续传输网络的计算模型 Monte Carlo Physarum Machine，可适用于稀疏的 2D 和 3D 数据，能够应用于宇宙学中的大尺度气体和暗物质的分布重建，并通过自体编排行为产生多种连续网络状的形态。

蒙特卡洛 Physarum 机器：连续随机传输网络中图案形成的特性

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

Modern cosmology predicts that matter in our Universe has assembled today
into a vast network of filamentary structures colloquially termed the Cosmic
Web. Because this matter is either electromagnetically invisible (i.e., dark)
or too diffuse to image in emission, tests of this cosmic web paradigm are
limited. Wide-field surveys do reveal web-like structures in the galaxy
distribution, but these luminous galaxies represent less than 10% of baryonic
matter. Statistics of absorption by the intergalactic medium (IGM) via
spectroscopy of distant quasars support the model yet have not conclusively
tied the diffuse IGM to the web. Here, we report on a new method inspired by
the Physarum polycephalum slime mold that is able to infer the density field of
the Cosmic Web from galaxy surveys. Applying our technique to galaxy and
absorption-line surveys of the local Universe, we demonstrate that the bulk of
the IGM indeed resides in the Cosmic Web. From the outskirts of Cosmic Web
filaments, at approximately the cosmic mean matter density (rho_m) and approx.
5 virial radii from nearby galaxies, we detect an increasing H I absorption
signature towards higher densities and the circumgalactic medium, to approx.
200 rho_m. However, the absorption is suppressed within the densest
environments, suggesting shock-heating and ionization deep within filaments
and/or feedback processes within galaxies.

利用 Physarum polycephalum 软泥菌的灵感，从星系调查中推断出宇宙网的密度场，并得出 IGM 大部分确实位于宇宙网中。

揭示宇宙蛛网的暗丝

Revealing the Dark Threads of the Cosmic Web

Matter evolved under influence of gravity from minuscule density
fluctuations. Non-perturbative structure formed hierarchically over all scales,
and developed non-Gaussian features in the Universe, known as the Cosmic Web.
To fully understand the structure formation of the Universe is one of the holy
grails of modern astrophysics. Astrophysicists survey large volumes of the
Universe and employ a large ensemble of computer simulations to compare with
the observed data in order to extract the full information of our own Universe.
However, to evolve trillions of galaxies over billions of years even with the
simplest physics is a daunting task. We build a deep neural network, the Deep
Density Displacement Model (hereafter D$^3$M), to predict the non-linear
structure formation of the Universe from simple linear perturbation theory. Our
extensive analysis, demonstrates that D$^3$M outperforms the second order
perturbation theory (hereafter 2LPT), the commonly used fast approximate
simulation method, in point-wise comparison, 2-point correlation, and 3-point
correlation. We also show that D$^3$M is able to accurately extrapolate far
beyond its training data, and predict structure formation for significantly
different cosmological parameters. Our study proves, for the first time, that
deep learning is a practical and accurate alternative to approximate
simulations of the gravitational structure formation of the Universe.

该研究使用深度学习模型解决宇宙结构形成的问题，并证明了这是一种实用和准确的替代方法。