Polyphorm: Structural Analysis of Cosmological Datasets via Interactive Physarum Polycephalum Visualization

This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycephalum, an unicellular slime mold organism that efficiently forages for nutrients, astrophysicists are able to extrapolate from sparse datasets, such as galaxy maps archived in the Sloan Digital Sky Survey, and then use these extrapolations to inform analyses of a wide range of other data, such as spectroscopic observations captured by the Hubble Space Telescope. Researchers can interactively update the simulation by adjusting model parameters, and then investigate the resulting visual output to form hypotheses about the data. We describe details of Polyphorm's simulation model and its interaction and visualization modalities, and we evaluate Polyphorm through three scientific use cases that demonstrate the effectiveness of our approach.

Automated summary

Polyphorm is an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets, inspired by the behavior of Physarum polycephalum. By extrapolating from sparse datasets and using interactive simulation, researchers can form hypotheses about the data and analyze a wide range of other data effectively.