AI for Art: paintings and music

• Johannes Hirn

Machine Learning at galactic and cosmological scales

• Pablo Villanueva

Machine learning provides us with a powerful tool in the fields of cosmology and astrophysics, allowing us to better understand how matter clusters and is distributed in the universe. In this talk, we review some relevant cosmological applications, focusing on two different methods. On the one hand, extracting the matter density field from 21 cm hydrogen maps making use of Convolutional Neural Networks (CNNs). On the other hand, inferring the total mass of a dark matter halo from a few properties of its hosted galaxies via Graph Neural Networks (GNNs). This provides a novel method to weigh real halos such as the Milky Way and Andromeda.

Reconstructing the halo clustering and halo mass function of N-body simulations using neural simulation-based inference

• Androniki Dimitriou

Dark Matter distribution in external galaxies with Machine Learning.

• Martín de Los Ríos

Signal-agnostic dark matter searches in direct detection data with machine learning

• Andre Scaffidi

Hyper-parameter’s uncertainty estimation for Machine Learning with Numerical Stochastic Perturbation Theory

• Guilherme Telo