Internship and thesis proposals
Connecting dark matter and galaxies with machine learning.

Domaines
Relativity/Astrophysics/Cosmology
Nuclear physics and Nuclear astrophysics

Type of internship
Théorique, numérique
Description
Event Dark matter (DM) is the greatest unsolved mysteries in cosmology and physics, and mostly hidden from the current observations. The DM distribution can be inferred from observed galaxy distributions, but their relation is complex. To learn the spatial correlation between DM and galaxies, we combine hydrodynamic simulations and machine learning techniques. Hydrodynamic simulations can predict the spatial correlation between DM and galaxies, which can be learned by state-of-the-art machine learning technique. This project aims to reveal the DM distribution in the real Universe using the current observation of galaxies

Contact
Michel Gonin
Laboratory : ILANCE - TOKYO - ITL 2014
Team : ILANCE
Team Website
/ Thesis :    Funding :