2003/11/28 by Trilce Estrada-Piedra, J. P. Torres-Papaqui, Estrada-Piedra, Trilce +10
Engineering · Physics and Astronomy · #Astronomical Observations and Instrumentation #Astronomy and Astrophysical Research #Astrophysics (astro-ph) #FOS: Physical sciences #Infrared Target Detection Methodologies #astro-ph
paper · pdf · doi:10.48550/arxiv.astro-ph/0311627
4 pages, 1 figure, To appear in Proceedings of the ADASS-XIII Conference in Strasbourg, October 2003
arxiv created 2003/11/28 · openalex publication_date 2003/11/28 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
We present a new technique to segregate old and young stellar populations in galactic spectra using machine learning methods. We used an ensemble of classifiers, each classifier in the ensemble specializes in young or old populations and was trained with locally weighted regression and tested using ten-fold cross-validation. Since the relevant information concentrates in certain regions of the spectra we used the method of sequential floating backward selection offline for feature selection. The application to Seyfert galaxies proved that this technique is very insensitive to the dilution by the Active Galactic Nucleus (AGN) continuum. Comparing with exhaustive search we concluded that both methods are similar in terms of accuracy but the machine learning method is faster by about two orders of magnitude.