A Datadriven Model of Nucleosynthesis with Chemical Tagging in a Lowerdimensional Latent Space
Abstract
Chemical tagging seeks to identify unique star formation sites from presentday 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 datadriven 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 modeled by a mixture of multivariate Gaussians in a lowerdimensional 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 informationtheoretic 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 = 2566 stars with 17 chemical abundances. We identify the rapid and slow neutroncapture processes, as well as latent factors consistent with Fepeak and αelement production, and another where K and Zn dominate. When we consider N ∼ 160,000 stars with missing abundances, we find another seven factors, as well as 16 components in latent space. Despite these components showing separation in chemistry, which 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 realize chemical tagging at a galacticscale. We release accompanying software that scales well with the available data, allowing for the model’s parameters to be optimized in seconds given a fixed number of latent factors, components, and ∼10^{7} abundance measurements.
 Publication:

The Astrophysical Journal
 Pub Date:
 December 2019
 DOI:
 10.3847/15384357/ab4fea
 arXiv:
 arXiv:1910.09811
 Bibcode:
 2019ApJ...887...73C
 Keywords:

 Bayesian statistics;
 Chemical abundances;
 Galaxy chemical evolution;
 1900;
 224;
 580;
 Astrophysics  Solar and Stellar Astrophysics;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 Accepted to ApJ