Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts

We test the validity of the cosmic distance duality relation (CDDR) by combining angular diameter distance and luminosity distance measurements from recent cosmological observations. For the angular diameter distance, we use data from transverse baryon acoustic oscillations and galaxy clusters. On the other hand, the luminosity distance is obtained from Type Ia supernovae in the Pantheon+ sample and from quasar catalogs. To reduce the large dispersion in quasar luminosity distances, we apply a selection criterion based on their deviation from the ΛCDM model and implement a binning procedure to suppress statistical noise. We reconstruct the CDDR using Gaussian Processes, a non-parametric supervised machine learning method. Our results show no significant deviation from the CDDR within the 2σ confidence level across the redshift range explored, supporting its validity even at high redshifts.

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Publication Details

Journal
Universe
Published
2025-09-09
DOI
https://doi.org/10.3390/universe11090307
Citations
1
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
1.15

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article

Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts

R. F. L. Holanda, Felipe Avila, Armando Bernui, Maria Lopes et al.
1 citations
Universe
Galaxies: Formation, Evolution, Phenomena
1.15
article

Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts

R. F. L. Holanda, Felipe Avila, Armando Bernui, Maria Lopes, Camila Franco, Fernanda Roman-Oliveira, Rafael C. Nunes
article en
1 citations

Abstract

We test the validity of the cosmic distance duality relation (CDDR) by combining angular diameter distance and luminosity distance measurements from recent cosmological observations. For the angular diameter distance, we use data from transverse baryon acoustic oscillations and galaxy clusters. On the other hand, the luminosity distance is obtained from Type Ia supernovae in the Pantheon+ sample and from quasar catalogs. To reduce the large dispersion in quasar luminosity distances, we apply a selection criterion based on their deviation from the ΛCDM model and implement a binning procedure to suppress statistical noise. We reconstruct the CDDR using Gaussian Processes, a non-parametric supervised machine learning method. Our results show no significant deviation from the CDDR within the 2σ confidence level across the redshift range explored, supporting its validity even at high redshifts.

UniverseVol. 11(9)
Universidade Federal do Rio Grande do Sul (BR), Universidade Federal do Rio Grande do Norte (BR), Universidade Federal de Campina Grande (BR), Valongo Observatory (BR), Instituto Nacional de Pesquisas Espaciais (BR)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Openalex Percentile: Top 16%
Galaxies: Formation, Evolution, Phenomena
1.15
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