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

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 α-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.

arXiv commentAccepted to ApJ

References in corpus (32)

• Astropy: A Community Python Package for Astronomy
• The NumPy array: a structure for efficient numerical computation
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• The New Galaxy: Signatures of its Formation
• Galactic chemical evolution: Carbon through Zinc
• The GALAH Survey: Scientific Motivation
• The Cannon: A data-driven approach to stellar label determination
• The GALAH Survey: Second Data Release
• The Payne: self-consistent ab initio fitting of stellar spectra
• The GALAH Survey: Observational Overview and Gaia DR1 companion
• Deep learning of multi-element abundances from high-resolution spectroscopic data
• The Long-term Evolution of the Galactic Disk Traced by Dissolving Star Clusters
• The influence of atomic diffusion on stellar ages and chemical tagging
• News from the Galactic suburbia: the chemical composition of the remote globular cluster NGC 2419
• The chemical homogeneity of open clusters
• The Bizarre Chemical Inventory of NGC 2419, An Extreme Outer Halo Globular Cluster
• Label Transfer from APOGEE to LAMOST: Precise Stellar Parameters for 450,000 LAMOST Giants
• Principal Component Analysis on Chemical Abundances Spaces
• Galactic Doppelganger: The chemical similarity among field stars and among stars with a common birth origin
• Tidal interactions between binary stars drives lithium production in low-mass red giants
• The RAVE-on catalog of stellar atmospheric parameters and chemical abundances for chemo-dynamic studies in the Gaia era
• Chemical tagging can work: Identification of stellar phase-space structures purely by chemical-abundance similarity
• Masses and Ages for 230,000 LAMOST Giants, via Their Carbon and Nitrogen Abundances
• Chemical signatures of the first star clusters
• Chemistry of the Most Metal-poor Stars in the Bulge and the z > 10 Universe
• Quantitative chemical tagging, stellar ages and the chemo-dynamical evolution of the Galactic disc
• The Cannon 2: A data-driven model of stellar spectra for detailed chemical abundance analyses
• On Potassium and Other Abundance Anomalies of Red Giants in NGC 2419
• The dimensionality of stellar chemical space using spectra from the Apache Point Observatory Galactic Evolution Experiment
• Metallicity-Dependent Galactic Isotopic Decomposition for Nucleosynthesis
• On the discovery of K-enhanced and possibly Mg-depleted stars throughout the Milky Way

Cited by in corpus (11)

• Asteroid Taxonomy from Cluster Analysis of Spectrometry and Albedo
• How magnetic activity alters what we learn from stellar spectra
• The GALAH Survey: Chemical Clocks
• The chemical enrichment of the Milky Way disk evaluated using conditional abundances
• Cool and Data-Driven: An Exploration of Optical Cool Dwarf Chemistry with Both Data-Driven and Physical Models
• The GALAH Survey: Dependence of elemental abundances on age and metallicity for stars in the Galactic disc
• SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes
• The Astrophysical Variance in Gaia-RVS Spectra
• Milky Way Mapper decoded abundances -- I. Shared disc enrichment patterns
• PISP: Projected-Space Inference of Stellar Parameters
• Tracing the assembly of the Milky Way's disk through abundance clustering