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A Sparse Gaussian Process Framework for Photometric Redshift Estimation

Abstract:

Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Simulated and real data from SDSS DR12 were used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learn...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/mnras/stv2425

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Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Sub department:
Astrophysics
Role:
Author
Keywords:
Pubs id:
pubs:570368
UUID:
uuid:7097dab7-448a-4835-ac06-285ce6b0eaa0
Local pid:
pubs:570368
Source identifiers:
570368
Deposit date:
2015-12-10

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