Estimating stellar atmospheric parameters based on LASSO and support-vector regression

Abstract

A scheme for estimating atmospheric parameters T${}_{eff}$, log$g$, and [Fe/H] is proposed on the basis of Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Haar wavelet. The proposed scheme consists of three processes. A spectrum is decomposed using the Haar wavelet transform and low-frequency components at the fourth level are considered as candidate features. Then, spectral features from the candidate features are detected using the LASSO algorithm to estimate the atmospheric parameters. Finally, atmospheric parameters are estimated from the extracted spectral features using the support-vector regression (SVR) method. The proposed scheme was evaluated using three sets of stellar spectra respectively from Sloan Digital Sky Survey (SDSS), Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), and Kurucz's model, respectively. The mean absolute errors are as follows: for 40~000 SDSS spectra, 0.0062 dex for log~T${}_{eff}$ (85.83 K for T${}_{eff}$), 0.2035 dex for log$g$ and 0.1512 dex for [Fe/H]; for 23963 LAMOST spectra, 0.0074 dex for log~T${}_{eff}$ (95.37 K for T${}_{eff}$), 0.1528 dex for log~$g$, and 0.1146 dex for [Fe/H]; and for 10469 synthetic spectra, 0.0010 dex for log T${}_{eff}$(14.42K for T${}_{eff}$), 0.0123 dex for log~$g$, and 0.0125 dex for [Fe/H].

9 pages, 8 figurs, 5 tables

References in corpus (6)

• The SEGUE Stellar Parameter Pipeline. I. Description and Initial Validation Tests
• 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
• The SEGUE Stellar Parameter Pipeline. IV. Validation with an Extended Sample of Galactic Globular and Open Clusters
• Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra
• SDSS/SEGUE Spectral Feature Analysis For Stellar Atmospheric Parameter Estimation

Cited by in corpus (5)

• Deriving the stellar labels of LAMOST spectra with Stellar LAbel Machine (SLAM)
• Estimating stellar atmospheric parameters, absolute magnitudes and elemental abundances from the LAMOST spectra with Kernel-based Principal Component Analysis
• J-PLUS: Support Vector Regression to Measure Stellar Parameters
• Photometric properties and stellar parameters of the rapidly rotating magnetic early-B star HD 345439
• The Stellar Spectra Factory (SSF) Based On SLAM