PyCon DE & PyData 2025

Guadalupe Canas Herrera

Guadalupe is a Theoretical Cosmologist working in understanding how the Universe began, how it evolved and what its ultimate fate could be. In particular, she is interested in studying alternative cosmological models with state-of-the-art astrophysical data using advanced statistical techniques and data science algorithms. Furthermore, she is interested in forecasting the performance of new experiments or new observables, for instance, Gravitational Waves.

She holds a Bachelor's in Physics from the University of Cantabria, and Master's and PhD degrees in Cosmology from Leiden University. Currently, she is a Research Fellow in Space Science at the European Space Agency. Moreover, she is an active member of the Euclid Consortium: the scientific group behind the data explotaition of the ESA Euclid mission. In particular, she is the maintainer of the code "Cosmology Likelihood for Observables in Euclid" or simply, CLOE. This software is part of the official data anlysics pipeline that will be eventually used to extract cosmological constraints of the Euclid data. Within the consortium, she is also co-leading the responsible group in charge of testing models beyond-Standard Cosmological Models to discernish the nature of Dark Matter or Dark Energy, or to test alternative inflationary models.


Session

04-24
09:05
45min
Chasing the Dark Universe with Euclid and Python: Unveiling the Secrets of the Cosmos
Guadalupe Canas Herrera

The ESA Euclid mission, launched in July 2023, is on a quest to unravel the mysteries of dark energy and dark matter: the enigmatic components that make up 95% of the Universe. By mapping one-third of the sky with unprecedented precision, Euclid is building the largest 3D map of the cosmos.

This talk explores how cosmologists bridge theory and and Euclid observation to reveal the hidden nature of dark energy and the dark matter. We will delve into the challenges of cosmological inference, where advanced statistical methods and Python-based pipelines compare theoretical models against Euclid's vast datasets, and we will explain how Bayesian inference, machine learning, and state-of-the-art simulations are revolutionizing our understanding of the cosmos.

Keynote
Zeiss Plenary (Spectrum)