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In the Galactic disk, stellar [Fe/H] and age predict orbits and precise [X/Fe]

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arxiv 1907.10606 v1 pith:PWC7Q7MX submitted 2019-07-24 astro-ph.SR astro-ph.GA

classification astro-ph.SRastro-ph.GA
keywords starsdiskgalacticorbitsalphaapogeedatagalactocentric
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abstract

We explore the structure of the element abundance--age--orbit distribution of the stars in the Milky Way's low-$\alpha$ disk, by (re-)deriving precise [Fe/H], [X/Fe] and ages, along with orbits, for red clump stars from the APOGEE survey. There has been a long-standing theoretical expectation and observational evidence that metallicity ([Fe/H]) and age are informative about a star's orbit, e.g. about its angular momentum and the corresponding mean Galactocentric distance or its vertical motion. Indeed, our analysis of the APOGEE data confirms that [Fe/H] or age alone can predict the stars' orbits far less well than the combination of the two. Remarkably, we find and show explicitly, that for known [Fe/H] and age, the other abundances [X/Fe] of Galactic disk stars can be predicted well (on average to 0.02 dex) across a wide range of Galactocentric radii, and therefore provide little additional information, e.g. for predicting their orbit. While the age-abundance space for metal poor stars and potentially for stars near the Galactic center is rich or complex, for the bulk of the Galaxy's low-$\alpha$ disk it is simple: [Fe/H] and age contain most information, unless [X/Fe] can be measured to 0.02, or better. Consequently, we do not have the precision with current (and likely near-future) data to assign stars to their individual (coeval) birth clusters, from which the disk is presumably formed. We can, however, place strong constraints on future models of galactic evolution, chemical enrichment and mixing.

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  1. Inferring Galactic Parameters from Chemical Abundances: A Multi-Star Approach

    astro-ph.GA 2019-09 conditional novelty 7.0 of 10

    The method recovers the IMF high-mass slope and Type Ia supernova rate to sub-percent precision from the chemical abundances of up to 200 simulated stars using a neural-network emulator and Hamiltonian Monte Carlo.

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