Kavli Affiliate: David Charbonneau
| First 5 Authors: Emily K. Pass, Emily K. Pass, , ,
| Summary:
As the field of exoplanetary astronomy has matured, demand has grown 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. Homogeneous
analyses are thus critical. Here we use the neural-net framework uberMS to
measure iron abundances and alpha enrichments 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 our method’s performance, 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 reliable parameter estimates with errors of roughly 100 K
in $T_rm eff$, 0.09 dex in $log g$, and 0.04 dex in [Fe/H] for many nearby
dwarf stars, although [Fe/H] performance is poorer for mid-to-late K dwarfs,
with the bias worsening with decreasing $T_rm eff$. Performance is also
worse for evolved stars. For [$alpha$/Fe], our error may be as good as 0.03
dex for dwarfs based on external benchmarks, despite sizable and variable
systematic differences when comparing with specific alpha-element abundances
from other catalogs.
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