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ANNz2 - Photometric redshift and probability density function estimation using machine-learning

Published online by Cambridge University Press:  01 July 2015

Iftach Sadeh
Iftach Sadeh*
Affiliation:
Astrophysics Group, Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom email: i.sadeh@ucl.ac.uk
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Abstract

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Large photometric galaxy surveys allow the study of questions at the forefront of science, such as the nature of dark energy. The success of such surveys depends on the ability to measure the photometric redshifts of objects (photo-zs), based on limited spectral data. A new major version of the public photo-z estimation software, ANNz, is presented here. The new code incorporates several machine-learning methods, such as artificial neural networks and boosted decision/regression trees, which are all used in concert. The objective of the algorithm is to dynamically optimize the performance of the photo-z estimation, and to properly derive the associated uncertainties. In addition to single-value solutions, the new code also generates full probability density functions in two independent ways.

Keywords

techniques: photometricgalaxies: distances and redshifts
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 316 - 318
Copyright
Copyright © International Astronomical Union 2015 

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