Getting something out of nothing: topological data analysis for cosmology.
Date: Tuesday, 13 December 2022
Time: 16:00 (UTC)
Speaker: Dr Jessi Cisewski Kehe (University of Wisconsin, USA)
Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au
Abstract:
The transference from data to information is a key component of many areas of research in astronomy and cosmology. This process can be challenging when data exhibit complicated spatial structures, such as the large-scale structure (LSS) of the Universe. Methods that target shape-related features may be helpful for summarizing qualitative properties that are not retrieved with standard techniques. Topological data analysis (TDA) provides a framework for quantifying shape-related properties of data. Persistent homology is a popular TDA tool that offers a procedure to represent, visualize, and interpret complex data by extracting topological features which may be used to infer properties of the underlying structures. Persistent homology is used to find different dimensional holes in a dataset across different scales, where zero-dimensional holes are clusters, one-dimensional holes are closed loops, two-dimensional holes are voids, and so on. The information is summarized in a persistence diagram, which may be used for further analysis such as visualization, inference, or classification. I will give an overview of persistent homology and discuss its use in some cosmology applications, such as discriminating LSS under varying cosmological assumptions.
