Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/32140
DC Field | Value | Language |
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dc.contributor.author | Razim, Oleksandra | en_US |
dc.contributor.author | CAVUOTI, STEFANO | en_US |
dc.contributor.author | BRESCIA, Massimo | en_US |
dc.contributor.author | RICCIO, GIUSEPPE | en_US |
dc.contributor.author | Salvato, Mara | en_US |
dc.contributor.author | Longo, Giuseppe | en_US |
dc.date.accessioned | 2022-05-31T14:22:46Z | - |
dc.date.available | 2022-05-31T14:22:46Z | - |
dc.date.issued | 2021 | en_US |
dc.identifier.issn | 0035-8711 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12386/32140 | - |
dc.description.abstract | In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for z<SUB>spec</SUB> < 1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable z<SUB>spec</SUB> that cause biases in the estimation of the photo-z algorithms' performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics. | en_US |
dc.language.iso | eng | en_US |
dc.title | Improving the reliability of photometric redshift with machine learning | en_US |
dc.identifier.doi | 10.1093/mnras/stab2334 | en_US |
dc.identifier.scopus | 2-s2.0-85117219609 | en_US |
dc.identifier.url | https://academic.oup.com/mnras/article-abstract/507/4/5034/6350583?redirectedFrom=fulltext&login=false | en_US |
dc.identifier.url | https://arxiv.org/abs/2108.04784 | en_US |
dc.relation.medium | STAMPA | en_US |
dc.relation.volume | 507 | en_US |
dc.relation.issue | 4 | en_US |
dc.relation.firstpage | 5034 | en_US |
dc.relation.lastpage | 5052 | en_US |
dc.relation.numberofpages | 19 | en_US |
dc.type.referee | REF_1 | en_US |
dc.description.numberofauthors | 6 | en_US |
dc.description.international | sì | en_US |
dc.contributor.country | ITA | en_US |
dc.contributor.country | DEU | en_US |
dc.relation.scientificsector | FIS/05 - ASTRONOMIA E ASTROFISICA | en_US |
dc.relation.journal | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | en_US |
dc.identifier.adsbibcode | 2021MNRAS.507.5034R | 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.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
crisitem.journal.journalissn | 0035-8711 | - |
crisitem.journal.ance | E112946 | - |
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 | 0000-0001-7020-1172 | - |
Appears in Collections: | 1.01 Articoli in rivista |
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File | Description | Size | Format | |
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stab2334.pdf | PDF editoriale | 7.51 MB | Adobe PDF | View/Open |
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