Speaker
Description
Cosmic voids are vast underdense regions in the universe that offer unique insights into dark energy and the large-scale structure of the cosmos. This talk presents a novel method for identifying voids in weak lensing convergence maps using a 2D watershed algorithm. By analyzing data from the CosmoGridV1 simulation and the Dark Energy Survey (DES), we demonstrate the algorithm's capability to detect voids and extract their statistical properties efficiently. Our results highlight the relationship between void characteristics and cosmological parameters, emphasizing the utility of void statistics as powerful cosmological probes. This approach provides a robust framework for future studies leveraging data from upcoming surveys like Euclid and LSST, with implications for advancing our understanding of cosmic evolution.
| Abstract title | Watershed Weak Lensing Voids |
|---|