Understanding the CGM in the context of large scale environment
A tenet of modern cosmology is the existence of the “cosmic web”, a vast filamentary structure formed via the collapse of matter due to gravity. This structure is ubiquitous in cosmological simulations yet challenging to observe due to its diffuse nature.
The Physarum polycephalum organism, a type of slime mold, has been used as an unconventional “biological computer” to solve a range of spatial problems, leveraging the organisms propensity to systematically explore its environment for food and to grow intricate yet natural networks. Examples include maze solving, shortest path finding, transportation system design and the travelling salesman problem, among others. Computational models of Physarum polycephalum, or virtual Physarum machines, have also been developed to model a range of problems, mimicking the organism’s foraging behavior, and adapting it to new contexts. Food sources thus become straightforward proxies for input data, and different chemical and physical stimuli are available as tunable parameters that steer the Physarum’s growth.
Recently, a new algorithm, the Monte Carlo Physarum Machine (MCPM), an agent-based virtual Physarum machine that algorithmically mimics the organism’s growth, was developed to model a low redshift (z < 0.01) sub-sample of the SDSS spectroscopic galaxy catalog (Burchet et al. 2020, Elek et al. 2020).
We have expanded the application of the MCPM to the classic SDSS and eBOSS surveys, mapping the large scale structure (LSS) to z < 0.5. The slime mold density map shown in the accompanying image was recently released as a value added catalog (VAC) and released by the SDSS DR17 team (Abdurro'uf et al. 2021, Wilde et al. in prep.). Applications of this density map include constraining the role of environment on galaxy evolution and the role of galactic feedback.
You can access the data cubes and the VAC galaxy catalogs here.