Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework

Abstract

In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we compare the stellar parameters determined using five popular synthetic spectral grids (INTRIGOSS, FERRE, AMBRE, PHOENIX, and MPIA/1DNLTE) with our convolutional neural network (CNN, $\text{StarNet}$). The stellar parameters are determined for six physical properties (effective temperature, surface gravity, metallicity, [$\alpha$/Fe], radial velocity, and rotational velocity) given the spectral resolution, signal-to-noise, and wavelength range of optical FLAMES-UVES spectra from the Gaia-ESO Survey. Both CNN modelling and epistemic uncertainties are incorporated through training an ensemble of networks. $\text{StarNet}$ training was also adapted to mitigate differences between the synthetic grids and observed spectra by augmenting with realistic observational signatures (i.e. resolution matching, wavelength sampling, Gaussian noise, zeroing flux values, rotational and radial velocities, continuum removal, and masking telluric regions). Using the FLAMES-UVES spectra for FGK type dwarfs and giants as a test set, we quantify the accuracy and precision of the stellar label predictions from $\text{StarNet}$. We find excellent results over a wide range of parameters when $\text{StarNet}$ is trained on the MPIA/1DNLTE synthetic grid, and acceptable results over smaller parameter ranges when trained on the 1DLTE grids. These tests also show that our CNN pipeline is highly adaptable to multiple simulation grids.

arXiv comment20 pages, 17 figures, published by MNRAS. Code for StarNet available at https://github.com/Spiffical/StarNet

References in corpus (73)

• The Sloan Digital Sky Survey: Technical Summary
• MESA Isochrones and Stellar Tracks (MIST). I: Solar-Scaled Models
• A new extensive library of PHOENIX stellar atmospheres and synthetic spectra
• X-shooter, the new wide band intermediate resolution spectrograph at the ESO Very Large Telescope
• SEGUE: A Spectroscopic Survey of 240,000 stars with g=14-20
• The Radial Velocity Experiment (RAVE): first data release
• Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Overview and Early Data
• ASPCAP: The Apogee Stellar Parameter and Chemical Abundances Pipeline
• A Spectroscopic Study of the Ancient Milky Way: F- and G-Type Stars in the Third Data Release of the Sloan Digital Sky Survey
• The Cannon: A data-driven approach to stellar label determination
• The RAdial Velocity Experiment (RAVE): Fourth data release
• The GALAH Survey: Second Data Release
• NLTE line formation of Fe for late-type stars. I. Standard stars with 1D and <3D> model atmospheres
• The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA Derived Quantities, Data Visualization Tools and Stellar Library
• Gaia FGK Benchmark Stars - Metallicity
• 4MOST: Project overview and information for the First Call for Proposals
• Gaia FGK Benchmark Stars: Effective temperatures and surface gravities
• APOGEE Data Releases 13 and 14: Data and Analysis
• The Payne: self-consistent ab initio fitting of stellar spectra
• Gaia-ESO Survey: The analysis of high-resolution UVES spectra of FGK-type stars
• NLTE abundances of Mn in a sample of metal-poor stars
• Abundance Estimates for 16 Elements in 6 Million Stars from LAMOST DR5 Low-Resolution Spectra
• Deep learning of multi-element abundances from high-resolution spectroscopic data
• A non-LTE study of neutral and singly-ionized calcium in late-type stars
• Non-LTE line formation of Fe in late-type stars - III. 3D non-LTE analysis of metal-poor stars
• Chromium: NLTE abundances in metal-poor stars and nucleosynthesis in the Galaxy
• Gaia FGK benchmark stars: abundances of alpha and iron-peak elements
• The Sixth Data Release of the Radial Velocity Experiment (RAVE) -- II: Stellar Atmospheric Parameters, Chemical Abundances and Distances
• Photometric redshift estimation via deep learning
• NLTE analysis of Co I/Co II lines in spectra of cool stars with new laboratory hyperfine splitting constants
• Automated derivation of stellar atmospheric parameters and chemical abundances: the MATISSE algorithm
• The R-Process Alliance: First Release from the Northern Search for r-Process Enhanced Metal-Poor Stars in the Galactic Halo
• The Gaia FGK Benchmark Stars - High resolution spectral library
• The AMBRE project: A new synthetic grid of high-resolution FGKM stellar spectra
• Ionization balance of Ti in the photospheres of the Sun and four late-type stars
• Label Transfer from APOGEE to LAMOST: Precise Stellar Parameters for 450,000 LAMOST Giants
• An Application of Deep Neural Networks in the Analysis of Stellar Spectra
• Red Supergiant Stars as Cosmic Abundance Probes: II. NLTE Effects in J-band Silicon Lines
• MUSE crowded field 3D spectroscopy of over 12,000 stars in the globular cluster NGC 6397 - I. The first comprehensive spectroscopic HRD of a globular cluster
• Discovery and Characterization of 3000+ Main-Sequence Binaries from APOGEE Spectra
• Center-to-Limb Variation of Solar 3-D Hydrodynamical Simulations
• Non-local thermodynamic equilibrium stellar spectroscopy with 1D and <3D> models - I. Methods and application to magnesium abundances in standard stars
• Accurate abundance analysis of late-type stars: advances in atomic physics
• A New Catalog of Globular Clusters in the Milky Way
• Signatures of unresolved binaries in stellar spectra: implications for spectral fitting
• Non-LTE line formation of Fe in late-type stars IV: Modelling of the solar centre-to-limb variation in 3D
• Binarity among CEMP-no stars: an indication of multiple formation pathways?
• Chemo-dynamics of outer halo dwarf stars, including \textit{Gaia}-Sausage and \textit{Gaia}-Sequoia candidates
• A collection of model stellar spectra for spectral types B to early-M
• The Pristine Survey -- VI. The first three years of medium-resolution follow-up spectroscopy of Pristine EMP star candidates
• The Gaia-ESO Survey: double, triple and quadruple-line spectroscopic binary candidates
• Red Supergiant Stars as Cosmic Abundance Probes: NLTE Effects in J-band Iron and Titanium Lines
• The Gaia-ESO Survey: calibration strategy
• NLTE Chemical abundances in Galactic open and globular clusters
• Red Supergiant Stars as Cosmic Abundance Probes. III. NLTE effects in J-band Magnesium lines
• Fundamental stellar parameters and metallicities from Bayesian spectroscopy. Application to low- and high-resolution spectra
• Pristine IX: CFHT ESPaDOnS Spectroscopic Analysis of 115 Bright Metal-Poor Candidate Stars
• The Cannon 2: A data-driven model of stellar spectra for detailed chemical abundance analyses
• The AMBRE Project: Stellar Parameterisation of the ESO:UVES archived spectra
• The AMBRE Project: r-process elements in the Milky Way thin and thick discs
• The Gaia-ESO Survey: detection and characterization of single line spectroscopic binaries
• BONNSAI: correlated stellar observables in Bayesian methods
• The Gaia FGK benchmark stars version 2.1
• Testing Stellar Population Fitting Ingredients with Globular Clusters I: Stellar Libraries
• Accelerated Fitting of Stellar Spectra
• Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks
• Influence of inelastic collisions with hydrogen atoms on the non-LTE line formation for Fe I and Fe II in the 1D model atmospheres of late-type stars
• Forecasting Chemical Abundance Precision for Extragalactic Stellar Archaeology
• Gaia-ESO Survey: INTRIGOSS - A new library of High Resolution Synthetic Spectra
• The Peculiar Globular Cluster Palomar 1 and Persistence in the SDSS-APOGEE Database
• Carbon-enhanced metal-poor stars in the SDSS-APOGEE database
• 4MOST Survey Strategy Plan

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• The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks
• Stellar Populations With Optical Spectra: Deep Learning vs. Popular Spectrum Fitting Codes
• Stellar Parameters and Chemical Abundances Estimated from LAMOST-II DR8 MRS based on Cycle-StarNet
• The Gaia-ESO Survey: Preparing the ground for 4MOST & WEAVE galactic surveys. Chemical evolution of lithium with machine learning
• Artificial Intelligence Assisted Inversion (AIAI) of Synthetic Type Ia Supernova Spectra
• Line-depth ratios as indicators of effective temperature and surface gravity
• A Model RRNet for Spectral Information Exploitation and LAMOST Medium-resolution Spectrum Parameter Estimation
• Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions
• Reliable stellar abundances of individual stars with the MUSE integral-field spectrograph
• SpectroTranslator: a deep-neural network algorithm to homogenize spectroscopic parameters
• The CARMENES search for exoplanets around M dwarfs -- A deep transfer learning method to determine Teff and [M/H] of target stars
• Parameter Estimation of LAMOST Medium-resolution Stellar Spectra
• StarUnLink: identifying and mitigating signals from communications satellites in stellar spectral surveys
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