The use of neural networks to probe the structure of the nearby universe

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dc.creatord'Abrusco, R.
dc.creatorLongo, G.
dc.creatorPaolillo, M.
dc.creatorde Filippis, E.
dc.creatorBrescia, M.
dc.creatorStaiano, A.
dc.creatorTagliaferri, R.
dc.date2007-01-05
dc.date.accessioned2026-07-07T07:38:43Z
dc.date.available2026-07-07T07:38:43Z
dc.descriptionIn the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs.
dc.description7 pages, 5 figures. To appear in the proceedings of the Astronomical Data Analysis -IV workshop held in Marseille in 2006. J.L. Starck et al. eds
dc.identifierhttps://arxiv.org/abs/astro-ph/0701137
dc.identifierhttp://arxiv.org/abs/astro-ph/0701137
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/121190
dc.subjectAstrophysics
dc.titleThe use of neural networks to probe the structure of the nearby universe
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