Razim, OleksandraOleksandraRazimCAVUOTI, STEFANOSTEFANOCAVUOTIBRESCIA, MassimoMassimoBRESCIARICCIO, GIUSEPPEGIUSEPPERICCIOSalvato, MaraMaraSalvatoLongo, GiuseppeGiuseppeLongo2022-05-312022-05-3120210035-8711http://hdl.handle.net/20.500.12386/32140In 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.STAMPAen10.1093/mnras/stab23342-s2.0-85117219609https://academic.oup.com/mnras/article-abstract/507/4/5034/6350583?redirectedFrom=fulltext&login=falsehttps://arxiv.org/abs/2108.047842021MNRAS.507.5034RFIS/05 - ASTRONOMIA E ASTROFISICA