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METAPHOR: Probability density estimation for machine learning based photometric redshifts

Published online by Cambridge University Press:  30 May 2017

V. Amaro ,
S. Cavuoti Open the ORCID record for S. Cavuoti [Opens in a new window] ,
M. Brescia ,
C. Vellucci ,
C. Tortora  and
G. Longo
V. Amaro
Affiliation:
Dept. of Physical Sciences, University of Napoli Federico II, via Cinthia 9, 80126 Napoli, Italy
S. Cavuoti
Affiliation:
INAF - Astronomical Observatory of Capodimonte, via Moiariello 16, 80131 Napoli, Italy
M. Brescia
Affiliation:
INAF - Astronomical Observatory of Capodimonte, via Moiariello 16, 80131 Napoli, Italy
C. Vellucci
Affiliation:
DIETI, University of Naples Federico II, Via Claudio, 21 I-80125 Napoli, Italy
C. Tortora
Affiliation:
Kapteyn Astronomical Institute, Univ. of Groningen, 9700 AV Groningen, the Netherlands
G. Longo
Affiliation:
Dept. of Physical Sciences, University of Napoli Federico II, via Cinthia 9, 80126 Napoli, Italy
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Abstract

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We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z’s and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF’s derived from a traditional SED template fitting method (Le Phare).

Keywords

techniques: photometric - galaxies: distances and redshifts - galaxies: photometry
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 197 - 200
Copyright
Copyright © International Astronomical Union 2017 

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