Issue |
A&A
Volume 423, Number 2, August IV 2004
|
|
---|---|---|
Page(s) | 761 - 776 | |
Section | Astronomical instrumentation | |
DOI | https://doi.org/10.1051/0004-6361:20040176 | |
Published online | 06 August 2004 |
Photometric redshifts with the Multilayer Perceptron Neural Network: Application to the HDF-S and SDSS
1
European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching, Germany e-mail: evanzell@eso.org
2
Dipartimento di Astronomia dell'Università di Padova, Vicolo dell'Osservatorio 2, 35122 Padova, Italy
3
INAF – Osservatorio Astronomico di Trieste, via GB Tiepolo 11, 40131 Trieste, Italy
4
INAF – Osservatorio Astronomico di Roma, via dell'Osservatorio 2, Monteporzio, Italy
5
Laboratoire d'Astrophysique de Marseille, Traverse du Siphon-Les trois Lucs, 13012 Marseille, France
6
INAF – Osservatorio Astronomico di Padova, Vicolo Osservatorio 5, 35122 Padova, Italy
7
Max-Planck-Institut fur extraterrestrische Physik, 85740 Garching, Germany
8
INAF – Osservatorio Astronomico di Brera, via Brera 28, 20121 Milano, Italy
Received:
27
February
2003
Accepted:
3
May
2004
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, ,
the agreement between the photometric and spectroscopic redshifts in the
HDF-S is good: the training on mixed data produces
,
showing that model libraries together with observed data
provide a sufficiently complete description of the galaxy population.
The neural system capability is also tested in a low redshift regime,
,
using the Sloan Digital Sky Survey
Data Release One (DR1) spectroscopic sample.
The resulting accuracy on 88 108 galaxies is
.
Inputs other than
galaxy colors – such as morphology, angular size and surface
brightness – may be easily incorporated in the neural network
technique. An important feature, in view of the application of the
technique to large databases, is the
computational speed: in the evaluation phase, redshifts of 105 galaxies are estimated in few seconds.
Key words: galaxies: distances and redshifts / methods: data analysis / techniques: photometric
© ESO, 2004
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