3 papers
astro-ph.CO2025
Bayesian Inference of Primordial Magnetic Field Parameters from CMB with Spherical Graph Neural Networks
Juan Alejandro Pinto Castro, Héctor J. Hortúa, Jorge Enrique García-Farieta +1
Deep learning has emerged as a transformative methodology in modern cosmology, providing powerful tools to extract meaningful physical information from complex astronomical dataset…
astro-ph.CO2025
Parameter sensitivity of cosmic pairwise velocities in the non-linear regime of structure formation
Jorge Enrique García-Farieta, Héctor J. Hortúa
The peculiar velocities of dark matter tracers drive the growth of cosmic structures, providing a sensitive test of cosmological models and strengthening constraints on the nature…
cs.LG2024
Forecasting VIX using Bayesian Deep Learning
Héctor J. Hortúa, Andrés Mora-Valencia
Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we le…