METAPHOR: Probability density estimation for machine learning based photometric redshifts
Date Issued
2017
Author(s)
Abstract
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).
Coverage
Astroinformatics
All editors
Brescia, Massimo; Djorgovski, Stanislav George; Feigelson, Eric, D.; Longo, Giuseppe; Cavuoti, Stefano
Series
PROCEEDINGS OF THE INTERNATIONAL ASTRONOMICAL UNION
Volume
vol. 12, S325
Start page
197
Conferenece
Astroinformatics
Conferenece place
Sorrento
Conferenece date
19-25 October, 2016
Issn Identifier
1743-9213
Ads BibCode
2017IAUS..325..197A
Rights
open.access