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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

      Affiliations
      Dpto. de Inteligencia Artificial (UNED), Madrid, Spain
      LAEFF–INTA, Madrid, Spain
      Conference
      April 2004
      April 2004
      : 1-6
      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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