Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/20.500.12386/27595
Campo DC | Valore | Lingua |
---|---|---|
dc.contributor.author | Bilicki, M. | en_US |
dc.contributor.author | Hoekstra, H. | en_US |
dc.contributor.author | Brown, M. J. I. | en_US |
dc.contributor.author | Amaro, V. | en_US |
dc.contributor.author | Blake, C. | en_US |
dc.contributor.author | CAVUOTI, STEFANO | en_US |
dc.contributor.author | de Jong, J. T. A. | en_US |
dc.contributor.author | Georgiou, C. | en_US |
dc.contributor.author | Hildebrandt, H. | en_US |
dc.contributor.author | Wolf, C. | en_US |
dc.contributor.author | Amon, A. | en_US |
dc.contributor.author | BRESCIA, Massimo | en_US |
dc.contributor.author | Brough, S. | en_US |
dc.contributor.author | Costa-Duarte, M. V. | en_US |
dc.contributor.author | Erben, T. | en_US |
dc.contributor.author | Glazebrook, K. | en_US |
dc.contributor.author | GRADO, ANIELLO | en_US |
dc.contributor.author | Heymans, C. | en_US |
dc.contributor.author | Jarrett, T. | en_US |
dc.contributor.author | Joudaki, S. | en_US |
dc.contributor.author | Kuijken, K. | en_US |
dc.contributor.author | Longo, G. | en_US |
dc.contributor.author | NAPOLITANO, NICOLA ROSARIO | en_US |
dc.contributor.author | Parkinson, D. | en_US |
dc.contributor.author | Vellucci, C. | en_US |
dc.contributor.author | Verdoes Kleijn, G. A. | en_US |
dc.contributor.author | Wang, L. | en_US |
dc.date.accessioned | 2020-10-06T06:08:40Z | - |
dc.date.available | 2020-10-06T06:08:40Z | - |
dc.date.issued | 2018 | en_US |
dc.identifier.issn | 0004-6361 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12386/27595 | - |
dc.description.abstract | 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 z<SUB>phot</SUB> ≲ 0.9 and r ≲ 23.5. At the bright end of r ≲ 20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-z derivation, as well as when VIKING and WISE infrared (IR) bands are added. While the fiducial four-band ugri setup gives a photo-z bias <δz/(1 + z)> = -2 × 10<SUP>-4</SUP> and scatter σ<SUB>δz/(1+z)</SUB> < 0.022 at mean <z> = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by 7% and the bias by an order of magnitude. Once the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 μm, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives <δz/(1 + z)> < 4 × 10<SUP>-5</SUP> and σ<SUB>δz/(1+z)</SUB> < 0.019. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimised for low-redshift studies such as galaxy-galaxy lensing, is limited to r ≲ 20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation. | en_US |
dc.language.iso | eng | en_US |
dc.title | Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks | en_US |
dc.type | Article | - |
dc.identifier.doi | 10.1051/0004-6361/201731942 | en_US |
dc.identifier.scopus | 2-s2.0-85051859213 | en_US |
dc.identifier.isi | 000442539800002 | en_US |
dc.identifier.url | http://arxiv.org/abs/1709.04205v2 | en_US |
dc.identifier.url | https://www.aanda.org/articles/aa/abs/2018/08/aa31942-17/aa31942-17.html | en_US |
dc.relation.medium | STAMPA | en_US |
dc.relation.volume | 616 | en_US |
dc.relation.firstpage | A69 | en_US |
dc.relation.numberofpages | 22 | en_US |
dc.relation.article | A69 | en_US |
dc.type.referee | REF_1 | en_US |
dc.description.numberofauthors | 27 | en_US |
dc.description.international | sì | en_US |
dc.contributor.country | ITA | en_US |
dc.contributor.country | GBR | en_US |
dc.contributor.country | FRA | en_US |
dc.contributor.country | DEU | en_US |
dc.contributor.country | NLD | en_US |
dc.relation.scientificsector | FIS/05 - ASTRONOMIA E ASTROFISICA | en_US |
dc.relation.journal | ASTRONOMY & ASTROPHYSICS | en_US |
dc.type.miur | 262 Articolo in rivista | - |
dc.identifier.adsbibcode | 2018A&A...616A..69B | en_US |
dc.relation.ercsector | ERC sectors::Physical Sciences and Engineering::PE9 Universe sciences: astro-physics/chemistry/biology; solar systems; stellar, galactic and extragalactic astronomy, planetary systems, cosmology, space science, instrumentation | en_US |
dc.description.apc | no | en_US |
dc.description.oa | 1 – prodotto con file in versione Open Access (allegare il file al passo 5-Carica) | en_US |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
crisitem.journal.journalissn | 0004-6361 | - |
crisitem.journal.ance | E016240 | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.orcid | 0000-0002-3787-4196 | - |
crisitem.author.orcid | 0000-0001-9506-5680 | - |
crisitem.author.orcid | 0000-0002-0501-8256 | - |
crisitem.author.orcid | 0000-0003-0911-8884 | - |
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