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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
Version:
Accepted manuscript

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Files:
Publisher copy:
10.1093/mnras/stv2425

Authors


Almosallam, IA More by this author
Lindsay, SN More by this author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Physics, Astrophysics
Roberts, SJ More by this author
Publisher:
Oxford University Press Publisher's website
Journal:
Monthly Notices of the Royal Astronomical Society Journal website
Publication date:
2015-11-05
DOI:
ISSN:
1365-2966
URN:
uuid:7097dab7-448a-4835-ac06-285ce6b0eaa0
Source identifiers:
570368
Local pid:
pubs:570368

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