On Spectral Classification and Astrophysical Parameter Estimation for Galactic Surveys

被引：0

作者：

Fiorentin, Paola Re ^{[1
,2
]}

Bailer-Jones, Coryn A. L. ^{[2
]}

Beers, Timothy C. ^{[3
,4
]}

Zwitter, Tomaz ^{[1
]}

机构：

[1] Univ Ljubljana, Dept Math & Phys, Jadranska 19, SLO-1000 Ljubljana, Slovenia

[2] Max Planck Inst Astron, D-68117 Heidelberg, Germany

[3] Michigan State Univ, CSCE, Dept Phys & Astron, E Lansing, MI 48824 USA

[4] Michigan State Univ, JINA Joint Inst Nuclear Astrophys, E Lansing, MI 48824 USA

来源：

CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS | 2008年 / 1082卷

基金：

美国国家科学基金会;

关键词：

Astronomical data bases: surveys; methods: data analysis; statistical; stars:; binaries; emission-line; fundamental parameters;

D O I：

暂无

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We present several strategies that are being developed in order to classify and parameterize individual stars observed by Galactic surveys, and illustrate some results obtained from spectra obtained by the RAdial Velocity Experiment (RAVE) and the Sloan Digital Sky Survey (SDSS/SEGUE). We demonstrate the efficiency of our models for discrete source classification and stellar atmospheric parameter estimation (effective temperature, surface gravity, and metallicity), which use supervised machine learning algorithms along with a principal component analysis front-end compression phase that also enables knowledge discovery.

引用

页码：76 / +

页数：2

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