Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment
Abstract
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. The goal of this work is to calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained with respect to the EAzY template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have emulated -- at brighter magnitude -- the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We have used a principal component analysis to graphically assess incompleteness and to relate it with the performance parameters provided by DNF. Finally, we have applied the results about the incompleteness to the photo-z computation on Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs for the galaxies on DES Deep Fields have been computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. They are available at https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have been developed to evaluate the performance in the absence of "true" redshift and to assess completeness. We have studied... (Partial abstract)
Keywords
Cite
@article{arxiv.2312.09721,
title = {Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment},
author = {L. Toribio San Cipriano and J. De Vicente and I. Sevilla-Noarbe and W. G. Hartley and J. Myles and A. Amon and G. M. Bernstein and A. Choi and K. Eckert and R. A. Gruendl and I. Harrison and E. Sheldon and B. Yanny and M. Aguena and S. S. Allam and O. Alves and D. Bacon and D. Brooks and A. Campos and A. Carnero Rosell and J. Carretero and F. J. Castander and C. Conselice and L. N. da Costa and M. E. S. Pereira and T. M. Davis and S. Desai and H. T. Diehl and P. Doel and I. Ferrero and J. Frieman and J. García-Bellido and E. Gaztañaga and G. Giannini and S. R. Hinton and D. L. Hollowood and K. Honscheid and D. J. James and K. Kuehn and S. Lee and C. Lidman and J. L. Marshall and J. Mena-Fernández and F. Menanteau and R. Miquel and A. Palmese and A. Pieres and A. A. Plazas Malagón and A. Roodman and E. Sanchez and M. Smith and M. Soares-Santos and E. Suchyta and M. E. C. Swanson and G. Tarle and M. Vincenzi and N. Weaverdyck and P. Wiseman},
journal= {arXiv preprint arXiv:2312.09721},
year = {2024}
}
Comments
14 pages, 17 figures