TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests

28 Mar 2013M. Carrasco KindR. J. Brunner

With the growth of large photometric surveys, accurately estimating photometric redshifts, preferably as a probability density function (PDF), and fully understanding the implicit systematic uncertainties in this process has become increasingly important. In this paper, we present a new, publicly available, parallel, machine learning algorithm that generates photometric redshift PDFs by using prediction trees and random forest techniques, which we have named TPZ... (read more)

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