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).
@article{arxiv.1703.02292,
title = {METAPHOR: Probability density estimation for machine learning based photometric redshifts},
author = {Valeria Amaro and Stefano Cavuoti and Massimo Brescia and Civita Vellucci and Crescenzo Tortora and Giuseppe Longo},
journal= {arXiv preprint arXiv:1703.02292},
year = {2017}
}
Comments
proceedings of the International Astronomical Union, IAU-325 symposium, Cambridge University press