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Harrison, I. Sevilla-Noarbe, J. Carretero, J. De Vicente, J. Frieman, J. Garc\\'ia-Bellido, J. L. Marshall, J. Mena-Fern\\'andez, J. Myles, K. Eckert, K. Honscheid, K. Kuehn, L. N. da Costa, L. Toribio San Cipriano, M. Aguena, M. E. C. Swanson, M. E. S. Pereira, M. Smith, M. Soares-Santos, M. Vincenzi, N. Weaverdyck, O. Alves, P. Doel, P. Wiseman, R. A. Gruendl, R. Miquel, S. Desai, S. Lee, S. R. Hinton, S. S. Allam, T. M. Davis, W. G. Hartley","submitted_at":"2023-12-15T11:49:34Z","abstract_excerpt":"Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. 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