ArborZ: Photometric Redshifts Using Boosted Decision Trees

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.

10 pages, 13 figures, submitted to ApJ

References in corpus (16)

• The Cosmic Evolution Survey (COSMOS) -- Overview
• COSMOS Photometric Redshifts with 30-bands for 2-deg2
• Galaxy Evolution from Halo Occupation Distribution Modeling of DEEP2 and SDSS Galaxy Clustering
• The All-wavelength Extended Groth Strip International Survey (AEGIS) Data Sets
• Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
• Galaxies in the Hubble Ultra Deep Field: I. Detection, Multiband Photometry, Photometric Redshifts, and Morphology
• Calibrating Redshift Distributions Beyond Spectroscopic Limits with Cross-Correlations
• The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA) and its first application: COSMOS
• The Galaxy Content of SDSS Clusters and Groups
• Estimating the Redshift Distribution of Faint Galaxy Samples
• The 2dF-SDSS LRG and QSO (2SLAQ) Luminous Red Galaxy Survey
• A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6
• Precision photometric redshift calibration for galaxy-galaxy weak lensing
• The Role of Environment in the Mass-Metallicity Relation
• Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
• Photometric Redshift Estimation Using Spectral Connectivity Analysis

Cited by in corpus (87)

• The DEEP2 Galaxy Redshift Survey: Design, Observations, Data Reduction, and Redshifts
• A Critical Assessment of Photometric Redshift Methods: A CANDELS Investigation
• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests
• PHAT: PHoto-z Accuracy Testing
• 2MASS Photometric Redshift catalog: a comprehensive three-dimensional census of the whole sky
• ANNz2 - photometric redshift and probability distribution function estimation using machine learning
• Photometric redshifts for the SDSS Data Release 12
• Photometric redshift analysis in the Dark Energy Survey Science Verification data
• The Dust-to-Gas and Dust-to-Metals Ratio in Galaxies from z=0-6
• Cosmological Constraints from Galaxy Clustering and the Mass-to-Number Ratio of Galaxy Clusters
• DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting
• SOMz: photometric redshift PDFs with self organizing maps and random atlas
• The galaxy UV luminosity function at z ~ 2 - 4; new results on faint-end slope and the evolution of luminosity density
• A Detailed Study of Photometric Redshifts for GOODS-South Galaxies
• A Measurement of the Correlation of Galaxy Surveys with CMB Lensing Convergence Maps from the South Pole Telescope
• A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters
• Shedding Light on the Galaxy Luminosity Function
• Using neural networks to estimate redshift distributions. An application to CFHTLenS
• Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
• Precise photometric redshifts with a narrow-band filter set: The PAU Survey at the William Herschel Telescope
• Anomaly detection for machine learning redshifts applied to SDSS galaxies
• The Blanco Cosmology Survey: An Optically-Selected Galaxy Cluster Catalog and a Public Release of Optical Data Products
• Redshift inference from the combination of galaxy colors and clustering in a hierarchical Bayesian model
• Feature importance for machine learning redshifts applied to SDSS galaxies
• PS1-STRM: Neural network source classification and photometric redshift catalogue for PS1 $3\pi$ DR1
• The Voronoi Tessellation cluster finder in 2+1 dimensions
• Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations
• Exhausting the Information: Novel Bayesian Combination of Photometric Redshift PDFs
• Morpho-z: improving photometric redshifts with galaxy morphology
• Photometric Redshift Probability Distributions for Galaxies in the SDSS DR8
• Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach
• Machine Learning and Cosmological Simulations I: Semi-Analytical Models
• Sample variance in photometric redshift calibration: cosmological biases and survey requirements
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• Redshift inference from the combination of galaxy colors and clustering in a hierarchical Bayesian model $-$ Application to realistic $N$-body simulations
• GAz: A Genetic Algorithm for Photometric Redshift Estimation
• ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys
• Data augmentation for machine learning redshifts applied to SDSS galaxies
• CLASH: Photometric redshifts with 16 HST bands in galaxy cluster fields
• Weak Lensing Tomographic Redshift Distribution Inference for the Hyper Suprime-Cam Subaru Strategic Program three-year shape catalogue
• The PAU Survey: Photometric redshifts using transfer learning from simulations
• Analysis of a Custom Support Vector Machine for Photometric Redshift Estimation and the Inclusion of Galaxy Shape Information
• The Next Generation Virgo Cluster Survey. XV. The photometric redshift estimation for background sources
• Pan-STARRS1 variability of XMM-COSMOS AGN. I. Impact on photometric redshifts
• Improving the reliability of photometric redshift with machine learning
• Extended Photometry for the DEEP2 Galaxy Redshift Survey: A Testbed for Photometric Redshift Experiments
• Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)
• Statistical Classification Techniques for Photometric Supernova Typing
• Assessing the photometric redshift precision of the S-PLUS survey: the Stripe-82 as a test-case
• The Efficacy of Galaxy Shape Parameters in Photometric Redshift Estimation: A Neural Network Approach
• Effect of training characteristics on object classification: an application using Boosted Decision Trees
• DES Science Portal: Computing Photometric Redshifts
• Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies
• Luminous red galaxies in the Kilo Degree Survey: selection with broad-band photometry and weak lensing measurements
• Photometric Classification of quasars from RCS-2 using Random Forest
• Machine Learning Based Real Bogus System for HSC-SSP Moving Object Detecting Pipeline
• Comparison of Observed Galaxy Properties with Semianalytic Model Predictions using Machine Learning
• Calibrating photometric redshift distributions with cross-correlations
• Improved Mock Galaxy Catalogs for the DEEP2 Galaxy Redshift Survey from Subhalo Abundance and Environment Matching
• Estimating redshift distributions using Hierarchical Logistic Gaussian processes
• Photometric Redshift Estimation for Quasars by Integration of KNN and SVM
• Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys
• A Composite Likelihood Approach for Inference under Photometric Redshift Uncertainty
• Galaxy clustering with photometric surveys using PDF redshift information
• Mixture Models for Photometric Redshifts
• GeneticKNN: A Weighted KNN Approach Supported by Genetic Algorithm for Photometric Redshift Estimation of Quasars
• The 2-degree Field Lensing Survey: photometric redshifts from a large new training sample to r<19.5
• Self-consistent redshift estimation using correlation functions without a spectroscopic reference sample
• Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case
• Photo-$z$ with CuBAN$z$: An improved photometric redshift estimator using Clustering aided Back Propagation Neural network
• Photo-z Quality Cuts and their Effect on the Measured Galaxy Clustering
• Photo-z-SQL: integrated, flexible photometric redshift computation in a database
• Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters
• Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data
• Tuning target selection algorithms to improve galaxy redshift estimates
• Estimation of Photometric Redshifts. II. Identification of Out-of-Distribution Data with Neural Networks
• Gaussian Mixture Models for Blended Photometric Redshifts
• The effects of UV photometry and binary interactions on photometric redshift and galaxy morphology
• Hawaii Two-0: High-redshift galaxy clustering and bias
• CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
• SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy: I. Architecture and Automated Redshift Measurement
• Machine learning applications in astrophysics: Photometric redshift estimation
• Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology
• Photometric Redshift Estimation Using Scaled Ensemble Learning
• Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods
• TOPz: Photometric redshifts using template fitting applied to the GAMA survey