102,109 research outputs found
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 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.Comment: 41 pages, 23 figures, to be published in A
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