A Data-driven Model of Nucleosynthesis with Chemical Tagging in a Lower-dimensional Latent Space

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 modeled 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 = 2566 stars with 17 chemical abundances. We identify the rapid- and slow neutron-capture processes, as well as latent factors consistent with Fe-peak and α-element 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 galactic-scale. 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 ~107 abundance measurements.We acknowledge support from the Australian Research Council through Discovery Project DP160100637. J.B.H. is supported by a Laureate Fellowship from the Australian Research Council. Parts of this research were supported by the Australian Research Council (ARC) Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. S.~B. acknowledges funds from the Alexander von Humboldt Foundation in the framework of the Sofja Kovalevskaja Award endowed by the Federal Ministry of Education and Research. S.B. is supported by the Australian Research Council (grants DP150100250 and DP160103747). S.L.M. acknowledges the support of the UNSW Scientia Fellowship program. J.D.S., S.L.M., and D.B.Z. acknowledge the support of the Australian Research Council through Discovery Project grant DP180101791. The Galah survey is based on observations made at the Australian Astronomical Observatory, under programmes A/2013B/13, A/2014A/25, A/2015A/19, and A/2017A/18. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present. This research has made use of NASA’s Astrophysics Data System