The Payne: self-consistent ab initio fitting of stellar spectra
Y. Ting, C. Conroy, H. Rix, and P. Cargile. (2018)cite arxiv:1804.01530Comment: 21 pages, 17 figures, 1 table, submitted to ApJ.
Abstract
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 $T_eff$ 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 (Cargile et al., in prep.): 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. This illustrates that The
Payne, brings us significantly closer to extracting the full information
content of stellar spectra with ab initio spectral models.
Description
[1804.01530] The Payne: self-consistent ab initio fitting of stellar spectra
%0 Generic
%1 ting2018payne
%A Ting, Yuan-Sen
%A Conroy, Charlie
%A Rix, Hans-Walter
%A Cargile, Phillip
%D 2018
%K fitting interpolation spectra stellar
%T The Payne: self-consistent ab initio fitting of stellar spectra
%U http://arxiv.org/abs/1804.01530
%X 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 $T_eff$ 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 (Cargile et al., in prep.): 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. This illustrates that The
Payne, brings us significantly closer to extracting the full information
content of stellar spectra with ab initio spectral models.
@misc{ting2018payne,
abstract = {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 (Cargile et al., in prep.): 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. This illustrates that The
Payne, brings us significantly closer to extracting the full information
content of stellar spectra with ab initio spectral models.},
added-at = {2018-04-06T11:19:59.000+0200},
author = {Ting, Yuan-Sen and Conroy, Charlie and Rix, Hans-Walter and Cargile, Phillip},
biburl = {https://www.bibsonomy.org/bibtex/2426d745cead51280a7065ad2fbc3e81b/miki},
description = {[1804.01530] The Payne: self-consistent ab initio fitting of stellar spectra},
interhash = {a3f526cde34cfdd6a3c3fde7bf0f704f},
intrahash = {426d745cead51280a7065ad2fbc3e81b},
keywords = {fitting interpolation spectra stellar},
note = {cite arxiv:1804.01530Comment: 21 pages, 17 figures, 1 table, submitted to ApJ},
timestamp = {2018-04-06T11:19:59.000+0200},
title = {The Payne: self-consistent ab initio fitting of stellar spectra},
url = {http://arxiv.org/abs/1804.01530},
year = 2018
}