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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Published inarXiv.org
Main Authors Lu, Honglin, Zhou, Hongyan, Wang, Junxian, Wang, Tinggui, Dong, Xiaobo, Zhuang, Zhenquan, Cheng, Li
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LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 09.10.2005
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Abstract 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.
AbstractList 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.
Author Wang, Junxian
Wang, Tinggui
Lu, Honglin
Cheng, Li
Zhou, Hongyan
Zhuang, Zhenquan
Dong, Xiaobo
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Snippet In this paper, we employe a new statistical analysis technique, Ensemble Learning for Independent Component Analysis (EL-ICA), on the synthetic galaxy spectra...
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SubjectTerms Galactic evolution
Galaxies
Independent component analysis
Mathematical models
Parameters
Sky surveys (astronomy)
Spectra
Star & galaxy formation
Star formation
Statistical analysis
Stellar mass
Title Ensemble Learning Independent Component Analysis of Normal Galaxy Spectra
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