Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS

Details Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS

2003-12-02

arxiv.org/abs/astro-ph/0312064v1

Astron.Astrophys. 423 (2004) 761-776

Astronomy and Astrophysics

Astrophysics

17 pages. Submitted to A&A in Feb. 2003, revised in Oct. 2003

Scientific paper

10.1051/0004-6361:20040176

We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep multicolor catalog. Various possible approaches for the training of the neural network are explored, including the deepest and most complete spectroscopic redshift catalog currently available (the Hubble Deep Field North dataset) and models of the spectral energy distribution of galaxies available in the literature. The MLP can be trained on observed data, theoretical data and mixed samples. The prediction of the method is tested on the spectroscopic sample in the HDF-S (44 galaxies). Over the entire redshift range, $0.1

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