Ensemble Learning Independent Component Analysis of Normal Galaxy Spectra

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dc.creatorLu, Honglin
dc.creatorZhou, Hongyan
dc.creatorWang, Junxian
dc.creatorWang, Tinggui
dc.creatorDong, Xiaobo
dc.creatorZhuang, Zhenquan
dc.creatorLi, Cheng
dc.date2005-10-09
dc.date.accessioned2026-07-07T11:24:50Z
dc.date.available2026-07-07T11:24:50Z
dc.descriptionIn 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.
dc.description41 pages, 23 figures, to be published in AJ
dc.identifierhttps://arxiv.org/abs/astro-ph/0510246
dc.identifierhttp://arxiv.org/abs/astro-ph/0510246
dc.identifierAstron.J.131:790-805,2006
dc.identifierdoi:10.1086/498711
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/195323
dc.subjectAstrophysics
dc.titleEnsemble Learning Independent Component Analysis of Normal Galaxy Spectra
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