How Many Elements Matter?
Yuan-Sen Ting, David H. Weinberg

TL;DR
This study uses advanced machine learning to analyze elemental abundances in Milky Way stars, revealing that many elements carry independent information despite high measurement precision, emphasizing the importance of direct measurements.
Contribution
It demonstrates that residual correlations among elemental abundances are sensitive indicators of underlying structure, requiring at least seven elements to fully capture their independence.
Findings
Residual scatter is consistent with measurement uncertainties.
Correlations between elements are too large to be due to measurement noise.
Many elements provide independent information about stellar populations.
Abstract
Some studies of stars' multi-element abundance distributions suggest at least 5-7 significant dimensions, but others show that many elemental abundances can be predicted to high accuracy from [Fe/H] and [Mg/Fe] (or [Fe/H] and age) alone. We show that both propositions can be, and are, simultaneously true. We adopt a machine learning technique known as normalizing flow to reconstruct the probability distribution of Milky Way disk stars in the space of 15 elemental abundances measured by APOGEE. Conditioning on Teff and log g minimizes the differential systematics. After further conditioning on [Fe/H] and [Mg/Fe], the residual scatter for most abundances is dex, consistent with APOGEE's reported statistical uncertainties of 0.01-0.015 dex and intrinsic scatter of 0.01-0.02 dex. Despite the small scatter, residual abundances display clear…
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