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

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L. Toribio San Cipriano , J. De Vicente , I. Sevilla-Noarbe , W. G. Hartley , J. Myles , A. Amon , G. M. Bernstein , A. Choi , K. Eckert , R. A. Gruendl , I. Harrison , E. Sheldon , B. Yanny , M. Aguena , S. Allam , O. Alves , David Bacon , D. Brooks , A. Campos , A. Carnero Rosell , J. Carretero , F. J. Castander , Christopher J. Conselice , L. N. da Costa , M. E. S. Pereira , T. M. Davis , S. Desai , H. T. Diehl , P. Doel , I. Ferrero , J. Frieman , J. García-Bellido , E. Gaztañaga , G. Giannini , S. R. Hinton , D. L. Hollowood , K. Honscheid , D. J. James , K. Kuehn , S. Lee , C. Lidman , J. L. Marshall , J. Mena-Fernández , F. Menanteau , R. Miquel , A. Palmese , A. Pieres , A. A. Plazas , A. Roodman , E. Sánchez , M. Smith , M. Soares-Santos , E. Suchyta , M. E. C. Swanson , G. Tarlé , M. Vincenzi , N. Weaverdyck , P. Wiseman ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2312.09721

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)

Related Topics

Computer Science

Deep Learning

Galaxy

Artificial Intelligence

Principal Component Analysis

Concepts

Redshift Context (archaeology) Computer science Deep learning Sample (material) Photometric redshift Galaxy Artificial intelligence Field (mathematics) Computation Machine learning Astrophysics Algorithm Key (lock) Principal component analysis Physics Mathematics Geology Thermodynamics Pure mathematics Paleontology Computer security

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Type: preprint
Language: en
Landing Page: http://hdl.handle.net/10261/379468
OA Status: green
References: 63
Related Works: 10
OpenAlex ID: https://openalex.org/W4389911937

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DOI: https://doi.org/10.48550/arxiv.2312.09721

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

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-12-15

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Authors: L. Toribio San Cipriano, J. De Vicente, I. Sevilla-Noarbe, W. G. Hartley, J. Myles, A. Amon, G. M. Bernstein, A. Choi, K. Eckert, R. A. Gruendl, I. Harrison, E. Sheldon, B. Yanny, M. Aguena, S. Allam, O. Alves, David Bacon, D. Brooks, A. Campos, A. Carnero Rosell, J. Carretero, F. J. Castander, Christopher J. Conselice, L. N. da Costa, M. E. S. Pereira, T. M. Davis, S. Desai, H. T. Diehl, P. Doel, I. Ferrero, J. Frieman, J. García-Bellido, E. Gaztañaga, G. Giannini, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, S. Lee, C. Lidman, J. L. Marshall, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, A. Pieres, A. A. Plazas, A. Roodman, E. Sánchez, M. Smith, M. Soares-Santos, E. Suchyta, M. E. C. Swanson, G. Tarlé, M. Vincenzi, N. Weaverdyck, P. Wiseman

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Concepts: Redshift, Context (archaeology), Computer science, Deep learning, Sample (material), Photometric redshift, Galaxy, Artificial intelligence, Field (mathematics), Computation, Machine learning, Astrophysics, Algorithm, Key (lock), Principal component analysis, Physics, Mathematics, Geology, Thermodynamics, Pure mathematics, Paleontology, Computer security

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cited_by_percentile_year
countries_distinct_count	10
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