Speaker
Jessi Cisewski-Kehe
(Yale University)
Description
Data exhibiting complicated spatial structures are common in many areas of science (e.g. cosmology, biology), but can be difficult to analyze. Persistent homology is a popular approach within the area of Topological Data Analysis (TDA) that offers a new way to represent, visualize, and interpret complex data by extracting topological features, which can be used to infer properties of the underlying structures. In particular, TDA may be useful for analyzing the large-scale structure (LSS) of the Universe, which is an intricate and spatially complex web of matter. In order to understand the physics of the Universe, theoretical and computational cosmologists develop large-scale simulations that allow for visualizing and analyzing the LSS under varying physical assumptions. Each point in the 3D data set represents a galaxy or a cluster of galaxies, and topological summaries ("persistent diagrams") can be obtained summarizing the different ordered holes in the data (e.g. connected components, loops, voids).
The topological summaries are interesting and informative descriptors of the Universe on their own, but hypothesis tests using the topological summaries would provide a way to make more rigorous comparisons of LSS under different theoretical models. For example, the received cosmological model has cold dark matter (CDM); however, while the case is strong for CDM, there are some observational inconsistencies with this theory. Another possibility is warm dark matter (WDM). It is of interest to see if a CDM Universe and WDM Universe produce LSS that is topologically distinct.
We present several possible test statistics for two-sample hypothesis tests using the topological summaries, carry out a simulation study to investigate the suitableness of the proposed test statistics using simulated data from a variation of the Voronoi foam model, and finally we apply the proposed inference framework to WDM vs. CDM cosmological simulation data.
Primary author
Jessi Cisewski-Kehe
(Yale University)
