November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

The halo-galaxy connection from a machine learning perspective

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster Paris Posters


Natália Rodrigues (Universidade de São Paulo)


The relationship between galaxies and halos is central to describing galaxy formation and a fundamental step toward extracting precise cosmological information from galaxy maps. However, this connection involves several complex processes that are interconnected. Machine learning methods are flexible tools that can learn complex correlations between a large number of features but are traditionally designed as deterministic estimators.
In this work, we use the IllustrisTNG300-1 simulation and investigate how machine learning methods capable of predicting distributions can accurately reproduce features of different galaxy populations based on their host halo properties. In particular, we study how the models can quantify the uncertainty related to the intrinsic scatter in the halo-galaxy connection.

Primary authors

Prof. Antonio Montero-Dorta (Universidad Técnica Federico Santa María) Ms Natalí de Santi (Universidade de São Paulo) Natália Rodrigues (Universidade de São Paulo) Prof. Raul Abramo (Universidade de São Paulo)

Presentation materials