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Cooperative photometric redshift estimation

Published online by Cambridge University Press:  30 May 2017

S. Cavuoti Open the ORCID record for S. Cavuoti [Opens in a new window] ,
C. Tortora ,
M. Brescia ,
G. Longo ,
M. Radovich ,
N. R. Napolitano ,
V. Amaro  and
C. Vellucci
S. Cavuoti
Affiliation:
INAF - Astronomical Observatory of Capodimonte, via Moiariello 16, I-80131 Napoli, Italy
C. Tortora
Affiliation:
Kapteyn Astronomical Institute, Univ. of Groningen, P.O. Box 800, 9700 AV Groningen, the Netherlands
M. Brescia
Affiliation:
INAF - Astronomical Observatory of Capodimonte, via Moiariello 16, I-80131 Napoli, Italy
G. Longo
Affiliation:
Department of Physics, University Federico II, Via Cinthia 6, I-80126 Napoli, Italy
M. Radovich
Affiliation:
INAF - Astronomical Observatory of Padua, vicolo dell’Osservatorio 5, I-35122 Padova, Italy
N. R. Napolitano
Affiliation:
INAF - Astronomical Observatory of Capodimonte, via Moiariello 16, I-80131 Napoli, Italy
V. Amaro
Affiliation:
Department of Physics, University Federico II, Via Cinthia 6, I-80126 Napoli, Italy
C. Vellucci
Affiliation:
DIETI, University of Naples Federico II, Via Claudio, 21 I-80125 Napoli, Italy
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Abstract

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In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of ~ 25,000 galaxies from the second data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.

Keywords

methods: data analysismethods: statisticalcatalogs
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 166 - 172
Copyright
Copyright © International Astronomical Union 2017 

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