A data-driven model of nucleosynthesis with chemical tagging in a lower-dimensional latent space

TL;DR

This paper introduces a novel data-driven model of nucleosynthesis using a lower-dimensional latent space to identify stellar formation sites through chemical tagging, effectively handling missing data and estimating key nucleosynthetic processes.

Contribution

It presents a new probabilistic model that captures correlated nucleosynthetic yields in a latent space, improving chemical tagging and understanding of stellar abundances.

Findings

Identified six latent factors explaining 2,566 stars with 17 abundances.

Discovered factors corresponding to neutron capture, Fe-peak, and alpha-element processes.

Extended analysis to 160,000 stars revealing additional latent components.

Abstract

Chemical tagging seeks to identify unique star formation sites from present-day stellar abundances. Previous techniques have treated each abundance dimension as being statistically independent, despite theoretical expectations that many elements can be produced by more than one nucleosynthetic process. In this work we introduce a data-driven model of nucleosynthesis where a set of latent factors (e.g., nucleosynthetic yields) contribute to all stars with different scores, and clustering (e.g., chemical tagging) is modelled by a mixture of multivariate Gaussians in a lower-dimensional latent space. We use an exact method to simultaneously estimate the factor scores for each star, the partial assignment of each star to each cluster, and the latent factors common to all stars, even in the presence of missing data entries. We use an information-theoretic Bayesian principle to estimate the number of latent factors and clusters. Using the second Galah data release we find that six latent factors are preferred to explain N = 2,566 stars with 17 chemical abundances. We identify the rapid- and slow-neutron capture processes, as well as latent factors consistent with Fe-peak and \alpha-element production, and another where K and Zn dominate. When we consider N ~ 160,000 stars with missing abundances we find another 7 factors, as well as 16 components in latent space. Despite these components showing separation in chemistry that is explained through different yield contributions, none show significant structure in their positions or motions. We argue that more data, and joint priors on cluster membership that are constrained by dynamical models, are necessary to realise chemical tagging at a galactic-scale. We release software that allows for model parameters to be optimised in seconds given a fixed number of latent factors, components, and $10^7$ abundance measurements.