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Neural Networks for Photometric Redshifts Evaluation

  • Roberto Tagliaferri
  • Giuseppe Longo
  • Stefano Andreon
  • Salvatore Capozziello
  • Ciro Donalek
  • Gerardo Giordano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2859)

Abstract

We present a neural network based approach to the determination of photometric redshift, which is a very important parameter to find the depth of astronomical objects in the sky. The method was tested on the Sloan Digital Sky Survey Early Data Release reaching an accuracy comparable and, in some cases, better than Spectral Energy Distribution template fitting techniques. We used Multi-Layer Perceptrons operating in a Bayesian framework to compute the parameter estimation, and a Self Organizing Map to estimate the accuracy of the results, evaluating the contamination between the classes of objects with a good prediction rate and with a poor one. In the best experiment, the implemented network reached an accuracy of 0.020 (robust error) in the range 0<z phot <0.3, and of 0.022 in the range 0<z phot <0.5.

Keywords

Bayesian Framework Multi Layer Perceptron Photometric Data Redshift Range Photometric Redshift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Roberto Tagliaferri
    • 1
    • 2
  • Giuseppe Longo
    • 3
  • Stefano Andreon
    • 4
  • Salvatore Capozziello
    • 5
  • Ciro Donalek
    • 2
    • 3
    • 6
  • Gerardo Giordano
    • 3
  1. 1.DMIUniversity of SalernoBaronissiItaly
  2. 2.INFMUnità di SalernoBaronissiItaly
  3. 3.Department of Physical SciencesUniversity Federico II of NaplesItaly
  4. 4.INAF-Osservatorio Astronomico di BreraMilano
  5. 5.Dipartimento di FisicaUniversità di SalernoBaronissiItaly
  6. 6.Dipartimento di Matematica ApplicataUniversity Federico II NaplesItaly

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