The Payne: self-consistent ab initio fitting of stellar spectra

We present The Payne, a general method for the precise and simultaneous determination of numerous stellar labels from observed spectra, based on fitting physical spectral models. The Payne combines a number of important methodological aspects: it exploits the information from much of the available spectral range; it fits all labels (stellar parameters and element abundances) simultaneously; it uses spectral models, where the atmosphere structure and the radiative transport are consistently calculated to reflect the stellar labels. At its core The Payne has an approach to accurate and precise interpolation and prediction of the spectrum in high-dimensional label-space, which is flexible and robust, yet based on only a moderate number of ab initio models (O(1000) for 25 labels). With a simple neural-net-like functional form and a suitable choice of training labels, this interpolation yields a spectral flux prediction good to $10^{-3}$ rms across a wide range of $T_{\rm eff}$ and log g (including dwarfs and giants). We illustrate the power of this approach by applying it to the APOGEE DR14 data set, drawing on Kurucz models with recently improved line lists: without recalibration, we obtain physically sensible stellar parameters as well as 15 element abundances that appear to be more precise than the published APOGEE DR14 values. In short, The Payne is an approach that for the first time combines all these key ingredients, necessary for progress towards optimal modelling of survey spectra; and it leads to both precise and accurate estimates of stellar labels, based on physical models and without re-calibration. Both the codes and catalog are made publicly available online.Comment: 22 pages, 17 figures, 2 tables, ApJ (Accepted for publication- 2019 May 11

From the Inner to Outer Milky Way: A Photometric Sample of 2.6 Million Red Clump Stars

Large pristine samples of red clump stars are highly sought after given that they are standard candles and give precise distances even at large distances. However, it is difficult to cleanly select red clumps stars because they can have the same T$_{\mathrm{eff}}$ and log $g$ as red giant branch stars. Recently, it was shown that the asteroseismic parameters, $\rm{\Delta}$P and $\rm{\Delta\nu}$, which are used to accurately select red clump stars, can be derived from spectra using the change in the surface carbon to nitrogen ratio ([C/N]) caused by mixing during the red giant branch. This change in [C/N] can also impact the spectral energy distribution. In this study, we predict the $\rm{\Delta}$P, $\rm{\Delta\nu}$, T$_{\mathrm{eff}}$ and log $g$ using 2MASS, AllWISE, \gaia, and Pan-STARRS data in order to select a clean sample of red clump stars. We achieve a contamination rate of $\sim$20\%, equivalent to what is achieved when selecting from T$_{\mathrm{eff}}$ and log $g$ derived from low resolution spectra. Finally, we present two red clump samples. One sample has a contamination rate of $\sim$ 20\% and $\sim$ 405,000 red clump stars. The other has a contamination of $\sim$ 33\% and $\sim$ 2.6 million red clump stars which includes $\sim$ 75,000 stars at distances $>$ 10 kpc. For |b|>30 degrees we find $\sim$ 15,000 stars with contamination rate of $\sim$ 9\%. The scientific potential of this catalog for studying the structure and formation history of the Galaxy is vast given that it includes millions of precise distances to stars in the inner bulge and distant halo where astrometric distances are imprecise.Comment: 18 pages, 13 figures, 2 tables, submitted to MNRA