Polyphorm: Structural Analysis of Cosmological Datasets via Interactive Physarum Polycephalum Visualization
This provides astrophysicists with a novel interactive tool for hypothesis formation from cosmological data, though it appears incremental as it builds on existing simulation methods.
The paper tackles the challenge of analyzing sparse cosmological datasets by introducing Polyphorm, an interactive tool that uses a fast computational simulation inspired by Physarum polycephalum to extrapolate from data like galaxy maps and inform analyses of other observations, such as those from the Hubble Space Telescope, with effectiveness demonstrated through three scientific use cases.
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.