Journal article
Photometric redshifts for the Kilo-Degree Survey Machine-learning analysis with artificial neural networks
- Abstract:
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We present a machine-learning photometric redshift (ML photo-z) analysis of the Kilo-Degree Survey Data Release 3 (KiDS DR3), using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the Bayesian Photometric Redshift (BPZ) code, at least up to zphot ≲ 0.9 and r ≲ 23.5. At the bright end of r ≲ 20, where very complete spectroscopic data overlappi...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.4MB, Terms of use)
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- Publisher copy:
- 10.1051/0004-6361/201731942
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Funding
Bibliographic Details
- Publisher:
- EDP Sciences
- Journal:
- Astronomy and Astrophysics More from this journal
- Volume:
- 616
- Pages:
- A69
- Publication date:
- 2018-08-21
- Acceptance date:
- 2018-04-30
- DOI:
- EISSN:
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1432-0746
- ISSN:
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0004-6361
Item Description
- Keywords:
- Pubs id:
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pubs:912121
- UUID:
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uuid:afeac417-7e26-4560-8aa4-cfa825a20ca9
- Local pid:
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pubs:912121
- Source identifiers:
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912121
- Deposit date:
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2018-10-06
Terms of use
- Copyright holder:
- European Southern Observatory
- Copyright date:
- 2018
- Notes:
- © ESO 2018. This is the published version of the article. This is also available online from EDP Sciences at: https://doi.org/10.1051/0004-6361/201731942
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