Causal Discovery of Latent Variables in Galactic Archaeology

TL;DR

This paper applies a novel causal discovery method to simulated galactic data, revealing hidden physical properties like birth radius from observable stellar data, advancing understanding of galaxy formation.

Contribution

It introduces Rank-based Latent Causal Discovery (RLCD) to identify latent variables in galactic archaeology using only observable data.

Findings

Successfully recovered latent variables related to star birth radius

Demonstrated causal structure can be inferred from limited observable properties

Showed potential of causal discovery in astrophysical research

Abstract

Galactic archaeology--the study of stellar migration histories--provides insights into galaxy formation and evolution. However, establishing causal relationships between observable stellar properties and their birth conditions remains challenging, as key properties like birth radius are not directly observable. We employ Rank-based Latent Causal Discovery (RLCD) to uncover the causal structure governing the chemodynamics of a simulated Milky Way galaxy. Using only five observable properties (metallicity, age, and orbital parameters), we recover in a purely data-driven manner a causal graph containing two latent nodes that correspond to real physical properties: the birth radius and guiding radius of stars. Our study demonstrates the potential of causal discovery models in astrophysics.