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Media type:
E-Article
Title:
Spectrophotometric Parallaxes with Linear Models: Accurate Distances for Luminous Red-giant Stars
Contributor:
Hogg, David W.;
Eilers, Anna-Christina;
Rix, Hans-Walter
Published:
American Astronomical Society, 2019
Published in:
The Astronomical Journal, 158 (2019) 4, Seite 147
Language:
Not determined
DOI:
10.3847/1538-3881/ab398c
ISSN:
0004-6256;
1538-3881
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title>
<jats:p>With contemporary infrared spectroscopic surveys like <jats:italic>APO Galactic Evolution Experiment</jats:italic>
<jats:italic> </jats:italic>(<jats:italic>APOGEE</jats:italic>), red-giant stars can be observed to distances and extinctions at which <jats:italic>Gaia</jats:italic> parallaxes are not highly informative. Yet the combination of effective temperature, surface gravity, composition, and age—all accessible through spectroscopy—determines a giant’s luminosity. Therefore spectroscopy plus photometry should enable precise spectrophotometric distance estimates. Here we use the overlap of <jats:italic>APOGEE, Gaia</jats:italic>, the Two Micron All Sky Survey (2MASS), the and <jats:italic>Wide-field Infrared Survey Explorer</jats:italic> (<jats:italic>WISE</jats:italic>) to train a data-driven model to predict parallaxes for red-giant branch stars with <jats:inline-formula>
<jats:tex-math>
<?CDATA $0\lt \mathrm{log}g\leqslant 2.2$?>
</jats:tex-math>
<jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ajab398cieqn1.gif" xlink:type="simple" />
</jats:inline-formula> (more luminous than the red clump). We employ (the exponentiation of) a linear function of <jats:italic>APOGEE</jats:italic> spectral pixel intensities and multiband photometry to predict parallax spectrophotometrically. The model training involves no logarithms or inverses of the <jats:italic>Gaia</jats:italic> parallaxes, and needs no cut on the <jats:italic>Gaia</jats:italic> parallax signal-to-noise ratio. It includes an L1 regularization to zero out the contributions of uninformative pixels. The training is performed with leave-out subsamples such that no star’s astrometry is used even indirectly in its spectrophotometric parallax estimate. The model implicitly performs a reddening and extinction correction in its parallax prediction, without any explicit dust model. We assign to each star in the sample a new spectrophotometric parallax estimate; these parallaxes have uncertainties of less than 15%, depending on data quality, which is more precise than the <jats:italic>Gaia</jats:italic> parallax for the vast majority of targets, and certainly any stars more than a few kiloparsec distance. We obtain 10% distance estimates out to heliocentric distances of 20 kpc, and make global maps of the Milky Way’s disk.</jats:p>