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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。宇宙中的所有星系都嵌在一个由物质丝状物组成的宇宙网络中。银河系天体物理学中的一个悬而未决的问题是，星系的结构和质量分布可能如何取决于它们所在的细丝的结构。这项工作将探索宇宙网络如何影响星系的结构和演化，进而探索星系演化和物质喷发可能如何影响宇宙网络。该项目将得到一个拉美裔服务机构的支持，并将吸引来自代表性不足群体的本科生研究人员。该小组还将在当地一所高中开展课后推广工作，在那里，高中生将有机会研究项目中的实际科学数据，并与项目人员进行交流。该团队将部署一种名为蒙特卡洛绒毛膜机(MCPM)的模拟程序，它包括丝状结构，同时提供环境密度估计，即使在星系间介质密度非常低的情况下也是如此。MCPM是一种计算模型，旨在重建稀疏离散数据上物理上可信的密度场-在这种情况下，这些数据主要是观测到的星系(位置和质量)，以及模拟的重子/暗物质晕。该模型以最优传输理论为基础，根据最小作用量原理得到了互连数据的网络式结构。考虑到最优运输问题的精确解决方案通常是NP难的，MCPM依赖于一种生物启发的解决方案：模拟多头绒泡菌的三维生长模式，这是已知的为这类问题提供了很好的近似。从计算的角度来看，MCPM是一种基于离散分量和连续分量组合的随机模型。离散组件是自由探索模拟领域的粒子状代理的集合。连续部分是一对3D标量网格：一个代表引导代理通过输入数据的环境标记的浓度，另一个累积代理的时空密度并代表实际解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.