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ASTRONOMY AND ASTROPHYSICS Neural networks and photometric redshifts

By R. Tagliaferri, G. Longo, S. Andreon, S. Capozziello, C. Donalek and G. Giordano


Abstract. We present a neural network based approach to the determination of photometric redshift. The method was tested on the Sloan Digital Sky Survey Early Data Release (SDSS-EDR) reaching an accuracy comparable and, in some cases, better than SED template fitting techniques. Different neural networks architecture have been tested and the combination of a Multi Layer Perceptron with 1 hidden layer (22 neurons) operated in a Bayesian framework, with a Self Organizing Map used to estimate the accuracy of the results, turned out to be the most effective. In the best experiment, the implemented network reached an accuracy of 0.020 (interquartile error) in the range 0 < zphot < 0.3, and of 0.022 in the range 0 < zphot < 0.5. Key words: data reduction – photometric redshifts – cosmologyA&A manuscript no. (will be inserted by hand later) Your thesaurus codes are: missing; you have not inserted the

Year: 2002
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