Parameterizing Stellar Spectra Using Deep Neural Networks

Abstract

This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural networks; second, the DNN is fine-tuned using a gradient descent scheme; third, stellar parameters ($T_{eff}$, log$g$, and [Fe/H]) are estimated using the obtained DNN. This scheme was evaluated on both real spectra from SDSS/SEGUE and synthetic spectra calculated from Kurucz's new opacity distribution function models. Test consistencies between our estimates and those provided by the spectroscopic parameter pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0048, 0.1477, and 0.1129 dex for log$T_{eff}$, log$g$, and [Fe/H] (64.85 K for $T_{eff}$), respectively. For the synthetic spectra, the MAE test accuracies are 0.0011, 0.0182, and 0.0112 dex for log$T_{eff}$, log$g$, and [Fe/H] (14.90 K for $T_{eff}$), respectively.

5 pages, 1 figure and 1 table

References in corpus (9)

• The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
• The First Data Release (DR1) of the LAMOST general survey
• 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
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• Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra
• SDSS/SEGUE Spectral Feature Analysis For Stellar Atmospheric Parameter Estimation
• Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters

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