Kavli Affiliate: David Charbonneau
| First 5 Authors: Emily K. Pass, Phillip A. Cargile, Victoria DiTomasso, Romy RodrÃguez MartÃnez, David Charbonneau
| Summary:
As the field of exoplanetary astronomy has matured, there has been growing
demand for precise stellar abundances to probe subtle correlations between
stellar compositions and planetary demographics. However, drawing
population-level conclusions from the disparate measurements in the literature
is challenging, with various groups measuring metallicities using bespoke codes
with differing line lists, radiative transfer models, and other assumptions.
Here we use the neural-net framework uberMS to measure iron abundances and
alpha enchriments from high-resolution optical spectra observed by the
Tillinghast Reflector Echelle Spectrograph (TRES), a key resource used for the
follow-up of candidate exoplanet hosts. To contextualize these measurements and
benchmark the performance of our method, we compare to external constraints on
metallicity using the Hyades cluster, wide binaries, and asteroids, to external
constraints on $T_{rm eff}$ and $log g$ using stars with interferometric
radii, and to the results of other abundance measurement methods using overlap
samples with the APOGEE and SPOCS catalogs, as well as by applying the SPC
method directly to TRES spectra. We find that TRES-uberMS provides parameter
estimates with errors of roughly 100K in $T_{rm eff}$, 0.09dex in $log g$,
and 0.04dex in [Fe/H] for nearby dwarf stars. However, [Fe/H] performance is
significantly poorer for mid-to-late K dwarfs, with the bias worsening with
decreasing $T_{rm eff}$. Performance is also worse for evolved stars. Our
[$alpha$/Fe] error may be as good as 0.03dex for dwarfs based on external
benchmarks, although there are large systematic differences when comparing with
specific alpha-element abundances from other catalogs.
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