Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/32140
Title: | Improving the reliability of photometric redshift with machine learning | Authors: | Razim, Oleksandra CAVUOTI, STEFANO BRESCIA, Massimo RICCIO, GIUSEPPE Salvato, Mara Longo, Giuseppe |
Issue Date: | 2021 | Journal: | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | Number: | 507 | Issue: | 4 | First Page: | 5034 | 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. | URI: | http://hdl.handle.net/20.500.12386/32140 | URL: | https://academic.oup.com/mnras/article-abstract/507/4/5034/6350583?redirectedFrom=fulltext&login=false https://arxiv.org/abs/2108.04784 |
ISSN: | 0035-8711 | DOI: | 10.1093/mnras/stab2334 | Bibcode ADS: | 2021MNRAS.507.5034R | Fulltext: | open |
Appears in Collections: | 1.01 Articoli in rivista |
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stab2334.pdf | PDF editoriale | 7.51 MB | Adobe PDF | View/Open |
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