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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*
Affiliation:
Astrophysics Group, Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom email: [email protected]
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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.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2015 

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