Blind nucleosynthetic source discovery in astronomical elemental abundance data

TL;DR

This paper introduces a data-driven method using non-negative matrix factorization to discover nucleosynthetic archetypes from incomplete stellar elemental abundance data, revealing patterns associated with different stellar sources.

Contribution

It presents a novel, physics-blind approach combining NMF and matrix completion to identify nucleosynthetic patterns without relying on astrophysical models.

Findings

Identified archetypes linked to AGB stars, supernovae, and r-process sources.

Successfully imputed missing elemental abundance measurements.

Demonstrated the method's ability to discern nucleosynthetic sources from incomplete data.

Abstract

We demonstrate physics-blind and purely data-based nucleosynthetic archetype discovery from heterogeneous stellar elemental abundance measurements. Archetype extraction is performed via non-negative matrix factorization (NMF). Because stellar elemental abundance measurements can be incomplete and sparsely sampled, we perform combined low-rank NMF and matrix completion on the Hypatia Catalog and JINAbase to extract archetypal elemental abundance patterns, the so-called endmembers, and impute the missing measurements. We qualitatively compare the discovered nucleosynthetic archetypes to patterns anticipated from direct astrophysical nucleosynthetic calculations. Our tentative results indicate that the physics-blind analysis relying entirely on the incomplete chemical mixing in star formation can discern nucleosynthetic archetypes associated with asymptotic giant branch (AGB) stellar sources of the s-process, thermonuclear supernovae, and non-thermonuclear supernovae with an r-process.