Astroseminar - Guadalupe Canas Herrera

Guadalupe is a Theoretical Cosmologist investigating the Universe's origins, evolution, and ultimate fate by studying alternative cosmological models with cutting-edge astrophysical data and advanced statistical techniques, while also forecasting the potential of new experiments and observables, such as Gravitational Waves. She holds a Bachelor's in Physics from the University of Cantabria (with exchange periods at Utrecht University and Brown University) and a Master's and PhD in Cosmology from Leiden University. Currently, she is a Research Fellow in Space Science at the European Space Agency and an active member of the Euclid Consortium, which analyzes data from the ESA Euclid mission. Within the consortium, Guadalupe maintains the "Cosmology Likelihood for Observables in Euclid" (CLOE) code, a key part of the official data analysis pipeline for extracting cosmological constraints, and co-leads the team testing models beyond the Standard Cosmological Model to explore the nature of Dark Matter, Dark Energy, and alternative inflationary scenarios.

Title: Diving into the era of Large-Scale Structure data: going beyond the Standard Cosmological Model

Abstract: In the next decade, the most precise constraints on the parameters of the ΛCDM model (or its extensions) will come from the information embedded in the large-scale structure (LSS) of the universe. The next generation of LSS surveys, such as Euclid, will open new opportunities for testing theoretical models. However, analyzing LSS data to obtain cosmological constraints presents clear challenges: high computational costs due to the sampling process, complex recipes for calculating theoretical observables, and the difficulty of modeling those observables at small scales. Furthermore, these challenges multiply if one aims to go beyond the ΛCDM scenario. In this talk, we will address these issues, highlighting key forecasting results from the Euclid Consortium, and how advanced computational techniques, such as machine learning, can help us work more efficiently in beyond-ΛCDM scenarios.