Neural Networks and Photometric Redshifts

Details Neural Networks and Photometric Redshifts Neural Networks and Photometric Redshifts

2002-03-25

arxiv.org/abs/astro-ph/0203445v2

Astronomy and Astrophysics

Astrophysics

9 pages, 5 figures, 3 tables

Scientific paper

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

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