ArborZ: Photometric Redshifts Using Boosted Decision Trees

TL;DR

ArborZ is a machine-learning algorithm using Boosted Decision Trees that improves photometric redshift estimates for galaxies, providing full PDFs and quality metrics, aiding cosmological studies from large imaging surveys.

Contribution

The paper introduces ArborZ, a novel photometric redshift method leveraging Boosted Decision Trees, offering improved accuracy and full probability density functions over existing algorithms.

Findings

ArborZ outperforms previous photometric redshift algorithms.

It provides full PDFs and quality metrics for each galaxy.

Stacked PDFs better reconstruct the redshift distribution N(z).

Abstract

Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of Boosted Decision Trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single "best estimate" and error, and also provides a photo-z quality figure-of-merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.