LEAPS-MPS: Unraveling the Galaxy-Cosmic Web Connection using Monte Carlo Physarum Machine

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。宇宙中所有的星系都镶嵌在物质细丝构成的宇宙网中。星系天体物理学中一个悬而未决的问题是星系的结构和质量分布如何取决于它们所嵌入的细丝的结构。这项工作将探索宇宙网如何影响星系的结构和演化，反过来，星系的演化和物质的排出如何影响宇宙网。该项目将在西班牙裔服务机构得到支持，并将从代表性不足的群体中吸引本科生研究人员。该团队还将在当地一所高中发起课外推广活动，在那里高中生将有机会处理项目中的实际科学数据，并与项目人员进行交流。该团队将部署一个名为蒙特卡洛绒泡体机（MCPM）的模拟程序，该程序结合了丝状结构，同时即使在星系际介质密度非常低的情况下也能提供环境密度估计。MCPM是一种计算模型，旨在通过稀疏的离散数据重建物理上合理的密度场——在这种情况下，这些主要是观测到的星系（位置和质量），或者模拟重子/暗物质晕。该模型以最优传输理论为基础，根据最小作用原理获得数据互连的网络结构。考虑到最优运输问题的精确解决方案通常是np困难的，MCPM依赖于一种受生物学启发的解决方案：模拟多头绒泡菌“黏菌”的三维生长模式，这种模式已知可以很好地近似解决这类问题。从计算的角度来看，MCPM是一个基于离散和连续分量相结合的随机模型。离散分量是粒子类代理的集合，可以自由地探索模拟域。连续分量是一对三维标量格：一个代表环境标记的浓度，引导代理通过输入数据，另一个累积代理的时空密度，代表实际解决方案。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). All of the galaxies in the Universe are embedded in a cosmic web of filaments of material. One of the open questions in galactic astrophysics is how the structure and mass distribution of galaxies might depend on the structure of the filaments in which they are embedded. This work will explore how the cosmic web affects the structure and evolution of galaxies, and in turn how galaxy evolution and the expelling of material might affect the cosmic web. The program will be supported at a Hispanic Serving Institution, and will draw undergraduate researchers from under-represented groups. The team will also initiate and after-school outreach effort at a local high school where high school students will have the opportunity to work on actual science data from the project, and interface with project personnel. The team will deploy a simulation routine called the Monte Carlo Physarum Machine (MCPM), which incorporates filamentary structures while providing environmental density estimates even at the very low densities of the intergalactic medium. The MCPM is a computational model designed to reconstruct physically plausible density fields over sparse discrete data – in this case, these are primarily observed galaxies (positions and masses), and alternatively simulated baryonic/dark matter halos. This model is underlined by the optimal transport theory, obtaining network-like structures that interconnect data according to the principle of least action. Given that exact solutions to optimal transport problems are typically NP-hard, MCPM relies on a biologically inspired solution: emulating three-dimensional growth patterns of Physarum polycephalum ‘slime mold’, which is known to provide good approximations to this class of problems. From a computational perspective, MCPM is a stochastic model based on a combination of a discrete and a continuous component. The discrete component is an ensemble of particle-like agents that freely explore the simulation domain. The continuous component is a pair of 3D scalar lattices: one that represents the concentration of an environmental marker that guides the agents through the input data, and one that accumulates the spatio-temporal density of the agents and represents the actual solution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.