Ensemble Learning Independent Component Analysis of Normal Galaxy Spectra

In this paper, we employe a new statistical analysis technique, Ensemble Learning for Independent Component Analysis (EL-ICA), on the synthetic galaxy spectra from a newly released high resolution evolutionary model by Bruzual & Charlot. We find that EL-ICA can sufficiently compress the syntheti...

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Bibliographic Details
Published inarXiv.org
Main Authors Lu, Honglin, Zhou, Hongyan, Wang, Junxian, Wang, Tinggui, Dong, Xiaobo, Zhuang, Zhenquan, Cheng, Li
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 09.10.2005
Subjects
Galactic evolution
Galaxies
Independent component analysis
Mathematical models
Parameters
Sky surveys (astronomy)
Spectra
Star & galaxy formation
Star formation
Statistical analysis
Stellar mass
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Summary:In this paper, we employe a new statistical analysis technique, Ensemble Learning for Independent Component Analysis (EL-ICA), on the synthetic galaxy spectra from a newly released high resolution evolutionary model by Bruzual & Charlot. We find that EL-ICA can sufficiently compress the synthetic galaxy spectral library to 6 non-negative Independent Components (ICs), which are good templates to model huge amount of normal galaxy spectra, such as the galaxy spectra in the Sloan Digital Sky Survey (SDSS). Important spectral parameters, such as starlight reddening, stellar velocity dispersion, stellar mass and star formation histories, can be given simultaneously by the fit. Extensive tests show that the fit and the derived parameters are reliable for galaxy spectra with the typical quality of the SDSS.
ISSN:2331-8422
DOI:10.48550/arxiv.0510246