Speaker
Description
Persistent homology is a powerful tool from the field of topological data analysis, which has shown promise as a novel statistic for cosmological parameter estimation. Compared with traditional two-point statistics, topological measurement presents information on a wide variety of scales and demonstrates a higher sensitivity to distinguish neutrino mass. We build a FLAMINGO-based topology emulator with 10 varying cosmological parameters and test the constraining power and degeneracy among them. Among these, the highest constraints come from $\beta_2$, which reveal the information in the void structure. Moreover, Betti curves can break the degeneracy between neutrino mass and other cosmological parameters through multi-environment detection. We plan to apply this method to observations, specifically weak lensing observations and galaxy clustering in redshift space using DESI data.
