ANNz2: Photometric Redshift and Probability Distribution Function Estimation using Machine Learning. (23rd August 2016)

• Record Type: Journal Article
• Authors: Sadeh, I.; Abdalla, F. B.; Lahav, O.
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 128:Number 968(2016)
• Publication Date: 2016-08-23
• Subjects: galaxies: distances and redshifts -- methods: data analysis; Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1088/1538-3873/128/968/104502
• Languages: English
• ISSNs: 0004-6280

Abstract

Abstract: We presentANNz2, a new implementation of the public software for photometric redshift (photo- z ) estimation of Collister & Lahav, which now includes generation of full probability distribution functions (PDFs).ANNz2 utilizes multiple machine learning methods, such as artificial neural networks and boosted decision/regression trees. The objective of the algorithm is to optimize the performance of the photo- z estimation, to properly derive the associated uncertainties, and to produce both single-value solutions and PDFs. In addition, estimators are made available, which mitigate possible problems of non-representative or incomplete spectroscopic training samples.ANNz2 has already been used as part of the first weak lensing analysis of the Dark Energy Survey, and is included in the experiment's first public data release. Here we illustrate the functionality of the code using data from the tenth data release of the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey. The code is available for download athttp://github.com/IftachSadeh/ANNZ .