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
Seminar Notice Board