SDSS/SEGUE Spectral Feature Analysis For Stellar Atmospheric Parameter Estimation

Abstract

Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and makes it feasible to statistically investigate latent principles in a large dataset. We present a technique for estimating parameters $T_{eff}$, log$g$ and [Fe/H] from stellar spectra. With this technique, we first extract features from stellar spectra using the LASSO algorithm; then, the parameters are estimated from the extracted features using the SVR. On a subsample of 20~000 stellar spectra from SDSS with reference parameters provided by SDSS/SEGUE Pipeline SSPP, estimation consistency are 0.007458 dex for log$T_{eff}$ (101.609921 K for $T_{eff}$), 0.189557 dex for log$g$ and 0.182060 for [Fe/H], where the consistency is evaluated by mean absolute error. Prominent characteristics of the proposed scheme are sparseness, locality, and physical interpretability. In this work, every spectrum consists of 3821 fluxes, and 10, 19, and 14 typical wavelength positions are detected respectively for estimating $T_{eff}$, log$g$ and [Fe/H]. It is shown that the positions are related to typical lines of stellar spectra. This characteristic is important in investigating physical indications from analysis results. Then, stellar spectra can be described by the individual fluxes on the detected positions (PD) or local integration of fluxes near them (LI). The abovementioned consistency is the result based on features described by LI. If features are described by PD, consistency are 0.009092 dex for log$T_{eff}$ (124.545075 K for $T_{eff}$), 0.198928 dex for log$g$, and 0.206814 dex for [Fe/H].

22 pages, 10 figures, 15 table, ApJ

References in corpus (29)

• The Sloan Digital Sky Survey: Technical Summary
• The Seventh Data Release of the Sloan Digital Sky Survey
• A grid of MARCS model atmospheres for late-type stars I. Methods and general properties
• The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey
• SEGUE: A Spectroscopic Survey of 240,000 stars with g=14-20
• GAIA: Composition, Formation and Evolution of the Galaxy
• The SEGUE Stellar Parameter Pipeline. I. Description and Initial Validation Tests
• ULySS: A Full Spectrum Fitting Package
• A Spectroscopic Study of the Ancient Milky Way: F- and G-Type Stars in the Third Data Release of the Sloan Digital Sky Survey
• Metallicity and Temperature Indicators in M dwarf K band Spectra: Testing New & Updated Calibrations With Observations of 133 Solar Neighborhood M dwarfs
• Empirical calibration of the near-IR Ca II triplet - I. The stellar library and index definition
• The SEGUE Stellar Parameter Pipeline. II. Validation with Galactic Globular and Open Clusters
• The SEGUE Stellar Parameter Pipeline. III. Comparison with High-Resolution Spectroscopy of SDSS/SEGUE Field Stars
• Vertical distribution of Galactic disk stars I - Kinematics and metallicity
• Coude-feed stellar spectral library - atmospheric parameters
• New grids of ATLAS9 atmospheres I: Influence of convection treatments on model structure and on observable quantities
• The SEGUE Stellar Parameter Pipeline. IV. Validation with an Extended Sample of Galactic Globular and Open Clusters
• Characterizing the Cool Kepler Objects of Interest. New Effective Temperatures, Metallicities, Masses and Radii of Low-Mass Kepler Planet-Candidate Host Stars
• The SEGUE Stellar Parameter Pipeline. V. Estimation of Alpha-Element Abundance Ratios From Low-Resolution SDSS/SEGUE Stellar Spectra
• Elemental abundances in the atmosphere of clump giants
• Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra
• Spectroscopic determination of the fundamental parameters of 66 B-type stars in the field-of-view of the CoRoT satellite
• Three-Dimensional Spectral Classification of Low-Metallicity Stars Using Artificial Neural Networks
• Analysis of medium resolution spectra by automated methods - application to M55 and omega Centauri
• Stellar atmosphere parameters with MAx, a MAssive compression of x^2 for spectral fitting
• Estimating Stellar Parameters from Spectra using a Hierarchical Bayesian Approach
• SPADES: a Stellar PArameters DEtermination Software
• A new synthetic library of the Near-Infrared CaII triplet indices. I.Index Definition, Calibration and Relations with stellar atmospheric parameters
• SPADES: Stellar Parameters Determination Software

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