  The elemental abundances of stars, particularly the refractory elements
(e.g., Fe, Si, Mg), play an important role in connecting stars to their
planets. Most Sun-like stars do not have refractory abundance measurements
since obtaining a large sample of high-resolution spectra is difficult with
oversubscribed observing resources. In this work we infer abundances for C, N,
O, Na, Mn, Cr, Si, Fe, Ni, Mg, V, Ca, Ti, Al, and Y for solar analogs with Gaia
RVS spectra (R=11,200) using the Cannon, a data-driven method. We train a
linear model on a reference set of 34 stars observed by Gaia RVS with precise
abundances measured from previous high resolution spectroscopic efforts (R >
30,000--110,000). We then apply this model to several thousand Gaia RVS solar
analogs. This yields abundances with average upper limit precisions of
0.04--0.1 dex for 17,412 stars, 50 of which are identified planet (candidate)
hosts. We subsequently test the relative refractory depletion of these stars
with increasing element condensation temperature compared to the Sun. The Sun
remains refractory depleted compared to other Sun-like stars regardless of our
current knowledge of the planets they host. This is inconsistent with theories
of various types of planets locking up or sequestering refractories.
Furthermore, we find no significant abundance differences between identified
close-in giant planet hosts, giant planet hosts, and terrestrial/small planet
hosts and the rest of the sample within our precision limits. This work
demonstrates the utility of data-driven learning for future exoplanet
composition and demographics studies.
