TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests
Year: 2013
Type: article
Abstract: 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.1 This new algorithm incorporates measurement errors into the calculation while also dealing efficiently with missing values in the data. In addition, our implementation of this algorithm provides supplementary information regarding the data being analysed, including unbiased estimates of the accuracy of the technique without resorting to a validation data set, identification of poor photometric redshift areas within the parameter space occupied by the spectroscopic training data, a quantification of the relative importance of the variables used to construct the PDF, and a robust identification of outliers. This extra information can be used to optimally target new spectroscopic observations and to improve the overall efficacy of the redshift estimation. We have tested TPZ on galaxy samples drawn from the Sloan Digital Sky Survey (SDSS) main galaxy sample and from the Deep Extragalactic Evolutionary Probe-2 (DEEP2) survey, obtaining excellent results in each case. We also have tested our implementation by participating in the PHAT1 project, which is a blind photometric redshift contest, finding that TPZ performs comparable to if not better than other empirical photometric redshift algorithms. Finally, we discuss the various parameters that control the operation of TPZ, the specific limitations of this approach and an application of photometric redshift PDFs.
DOI: 10.1093/mnras/stt574
Authors: Matias Carrasco Kind, Robert J. Brunner
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Aeronautics and Space Administration, Princeton University, Ohio State University, University of Washington, Johns Hopkins University, University of Pittsburgh, Drexel University, Universität Basel, University of Portsmouth, New Mexico State University, Chinese Academy of Sciences, Max-Planck-Institut für Astronomie, University of Illinois at Urbana-Champaign, U.S. Naval Observatory, Fermilab, Los Alamos National Laboratory
Awards: G1269417628, G1551047073, The DEEP Survey of the Distant Universe, Collaborative Research: The DEEP Survey--Emergence of the Modern Universe, XSEDE: eXtreme Science and Engineering Discovery Environment, Collaborative Research: The DEEP Survey--Emergence of the Modern Universe, STAR FORMATION AND GALAXY DYNAMICS AT Z-1: SIRTF, GALEX, & THE DEEP2 GALAXY REDSHIFT SURVEY
OpenAlex ID: W2015990127