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Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Fuente: arXiv

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Bibliographic Details
Main Authors:	Cipriano, L. Toribio San, De Vicente, J., Sevilla-Noarbe, I., Hartley, W. G., Myles, J., Amon, A., Bernstein, G. M., Choi, A., Eckert, K., Gruendl, R. A., Harrison, I., Sheldon, E., Yanny, B., Aguena, M., Allam, S. S., Alves, O., Bacon, D., Brooks, D., Campos, A., Rosell, A. Carnero, Carretero, J., Castander, F. J., Conselice, C., da Costa, L. N., Pereira, M. E. S., Davis, T. M., Desai, S., Diehl, H. T., Doel, P., Ferrero, I., Frieman, J., García-Bellido, J., Gaztañaga, E., Giannini, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lee, S., Lidman, C., Marshall, J. L., Mena-Fernández, J., Menanteau, F., Miquel, R., Palmese, A., Pieres, A., Malagón, A. A. Plazas, Roodman, A., Sanchez, E., Smith, M., Soares-Santos, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Vincenzi, M., Weaverdyck, N., Wiseman, P.
Format:	Preprint
Published:	2023
Subjects:	Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Online Access:	Acceder al recurso
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author	Cipriano, L. Toribio San De Vicente, J. Sevilla-Noarbe, I. Hartley, W. G. Myles, J. Amon, A. Bernstein, G. M. Choi, A. Eckert, K. Gruendl, R. A. Harrison, I. Sheldon, E. Yanny, B. Aguena, M. Allam, S. S. Alves, O. Bacon, D. Brooks, D. Campos, A. Rosell, A. Carnero Carretero, J. Castander, F. J. Conselice, C. da Costa, L. N. Pereira, M. E. S. Davis, T. M. Desai, S. Diehl, H. T. Doel, P. Ferrero, I. Frieman, J. García-Bellido, J. Gaztañaga, E. Giannini, G. Hinton, S. R. Hollowood, D. L. Honscheid, K. James, D. J. Kuehn, K. Lee, S. Lidman, C. Marshall, J. L. Mena-Fernández, J. Menanteau, F. Miquel, R. Palmese, A. Pieres, A. Malagón, A. A. Plazas Roodman, A. Sanchez, E. Smith, M. Soares-Santos, M. Suchyta, E. Swanson, M. E. C. Tarle, G. Vincenzi, M. Weaverdyck, N. Wiseman, P.
author_facet	Cipriano, L. Toribio San De Vicente, J. Sevilla-Noarbe, I. Hartley, W. G. Myles, J. Amon, A. Bernstein, G. M. Choi, A. Eckert, K. Gruendl, R. A. Harrison, I. Sheldon, E. Yanny, B. Aguena, M. Allam, S. S. Alves, O. Bacon, D. Brooks, D. Campos, A. Rosell, A. Carnero Carretero, J. Castander, F. J. Conselice, C. da Costa, L. N. Pereira, M. E. S. Davis, T. M. Desai, S. Diehl, H. T. Doel, P. Ferrero, I. Frieman, J. García-Bellido, J. Gaztañaga, E. Giannini, G. Hinton, S. R. Hollowood, D. L. Honscheid, K. James, D. J. Kuehn, K. Lee, S. Lidman, C. Marshall, J. L. Mena-Fernández, J. Menanteau, F. Miquel, R. Palmese, A. Pieres, A. Malagón, A. A. Plazas Roodman, A. Sanchez, E. Smith, M. Soares-Santos, M. Suchyta, E. Swanson, M. E. C. Tarle, G. Vincenzi, M. Weaverdyck, N. Wiseman, P.
contents	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)
format	Preprint
id	arxiv_https___arxiv_org_abs_2312_09721
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment Cipriano, L. Toribio San De Vicente, J. Sevilla-Noarbe, I. Hartley, W. G. Myles, J. Amon, A. Bernstein, G. M. Choi, A. Eckert, K. Gruendl, R. A. Harrison, I. Sheldon, E. Yanny, B. Aguena, M. Allam, S. S. Alves, O. Bacon, D. Brooks, D. Campos, A. Rosell, A. Carnero Carretero, J. Castander, F. J. Conselice, C. da Costa, L. N. Pereira, M. E. S. Davis, T. M. Desai, S. Diehl, H. T. Doel, P. Ferrero, I. Frieman, J. García-Bellido, J. Gaztañaga, E. Giannini, G. Hinton, S. R. Hollowood, D. L. Honscheid, K. James, D. J. Kuehn, K. Lee, S. Lidman, C. Marshall, J. L. Mena-Fernández, J. Menanteau, F. Miquel, R. Palmese, A. Pieres, A. Malagón, A. A. Plazas Roodman, A. Sanchez, E. Smith, M. Soares-Santos, M. Suchyta, E. Swanson, M. E. C. Tarle, G. Vincenzi, M. Weaverdyck, N. Wiseman, P. Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies 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)
title	Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment
topic	Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
url	https://arxiv.org/abs/2312.09721