2023 Project Description

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Detection of halo assembly bias using machine learning

Keywords:

cosmology

External Funding

galaxy formation

galaxy-halo connection

machine learning

Supervisors

Yen-Ting Lin; Chen-Yu Chuang
Find out more about supervisors on ASIAA website

Task Description and Goals

We believe galaxies are formed within dark matter halos. To understand galaxy formation, one thus must have a good handle on the properties of their host dark matter halos. A fundamental prediction of the standard cosmology model is the existence of the so-called assembly bias (AB) phenomenon, which has been proven to be extremely challenging to detect observationally. In this project, we will try to detect AB in the real world by applying a Graph Neural Network model that can predict the dark matter halo properties based on the observed galaxy properties (such as stellar mass, color, shape, etc).

Required Background

Fluent in python or/and c/c++; background in machine learning preferred.