Ensemble Learning for Independent Component Analysis of Normal Galaxy Spectra | Zendy

HongLin Lu | Zendy; Hongyan Zhou | Zendy; Junxian Wang | Zendy; Tinggui Wang | Zendy; Xiao-Bo Dong | Zendy; Zhenquan Zhuang | Zendy; Cheng Li | Zendy

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Ensemble Learning for Independent Component Analysis of Normal Galaxy Spectra

Author(s) -

HongLin Lu,

Hongyan Zhou,

Junxian Wang,

Tinggui Wang,

Xiao-Bo Dong,

Zhenquan Zhuang,

Cheng Li

Publication year - 2006

Publication title -

the astronomical journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.61

H-Index - 271

eISSN - 1538-3881

pISSN - 0004-6256

DOI - 10.1086/498711

Subject(s) - physics , galaxy , astrophysics , sky , independent component analysis , spectral line , galaxy formation and evolution , starlight , velocity dispersion , component (thermodynamics) , astronomy , stars , computer science , artificial intelligence , thermodynamics

In this paper, we employe a new statistical analysis technique, EnsembleLearning for Independent Component Analysis (EL-ICA), on the synthetic galaxyspectra from a newly released high resolution evolutionary model by Bruzual &Charlot. We find that EL-ICA can sufficiently compress the synthetic galaxyspectral library to 6 non-negative Independent Components (ICs), which are goodtemplates to model huge amount of normal galaxy spectra, such as the galaxyspectra in the Sloan Digital Sky Survey (SDSS). Important spectral parameters,such as starlight reddening, stellar velocity dispersion, stellar mass and starformation histories, can be given simultaneously by the fit. Extensive testsshow that the fit and the derived parameters are reliable for galaxy spectrawith the typical quality of the SDSS.Comment: 41 pages, 23 figures, to be published in A

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