METAPHOR: Probability density estimation for machine learning based photometric redshifts

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...

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Published in:arXiv.org 2017-03
Main Authors: Amaro, Valeria, Cavuoti, Stefano, Brescia, Massimo, Vellucci, Civita, Tortora, Crescenzo, Longo, Giuseppe
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Language:English
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Galaxies
Machine learning
Neural networks
Photometry
Workflow
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Cavuoti, Stefano
Brescia, Massimo
Vellucci, Civita
Tortora, Crescenzo
Longo, Giuseppe
description 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).
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subjects Galaxies
Machine learning
Neural networks
Photometry
Workflow
title METAPHOR: Probability density estimation for machine learning based photometric redshifts
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