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      Learning from optically variable stars: the OMC scientific case

      1 , 2 , 2 , 2 , 2

      ADA-III - Astronomical Data Analysis III Conference (ADA)

      Astronomical Data Analysis

      29 April - 1 May 2004

      Artificial Neural Networks, Bayesian Methods, Variable stars

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          Abstract

          In this work we present ongoing lines of research carried out by the Group of Operations and Archives at LAEFF (Laboratory for Space Astrophysics and Fundamental Physics) in order to provide “intelligent” tools for the scientific exploitation of the astronomical archive data provided by its Scientific Data Center. We focus here in the problems of detecting non periodic variability and classifying periodic light curves in the ever increasing archive of the Optical Moniting Camera (hereafter OMC) on board INTEGRAL.

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          Author and article information

          Conference
          April 2004
          April 2004
          : 1-6
          Affiliations
          Dpto. de Inteligencia Artificial (UNED), Madrid, Spain
          LAEFF–INTA, Madrid, Spain
          10.14236/ewic/ADA-III2004.4
          © Luis Manuel Sarro et al. Published by BCS Learning and Development Ltd. ADA-III - Astronomical Data Analysis III Conference, Sant' Agata sui due Golfi, Italy

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          ADA-III - Astronomical Data Analysis III Conference
          ADA
          3
          Sant' Agata sui due Golfi, Italy
          29 April - 1 May 2004
          Electronic Workshops in Computing (eWiC)
          Astronomical Data Analysis
          Product
          Product Information: 1477-9358 BCS Learning & Development
          Self URI (journal page): https://ewic.bcs.org/
          Categories
          Electronic Workshops in Computing

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