Speaker
Description
Large underdense regions in the Large Scale Structure (LSS) of the Universe, also known as voids, are a prominent features of the cosmic web that can also be used as a cosmological probe. In this talk, I will present a new void-finding algorithm that can be applied to both observational and simulated data. The algorithm is based on the $\beta$-Skeleton, an algorithm widely used on machine learning, optimization and image recognition; recently it has been introduced as a LSS analysis tool. The analysis we have performed, on observational and simulated data, considers voids as ellipsoids. We study their statistical properties such as volumes and shape finding a good agreement with other void finders. We finalize by exploring possible applications of this void finder to constrain cosmological parameters based on data from the Dark Energy Spectroscopic Instrument.
