Estimation of the photometric redshift of galaxies using artificial neural networks

Authors

DOI:

https://doi.org/10.47456/Cad.Astro.v7nEspecial.53390

Keywords:

Galaxies, Redshift, Neural Networks

Abstract

This work was presented at the event "The Astro-Scientists: III Brazilian Meeting of Girls and Women in Astrophysics, Cosmology and Gravitation", held between March 31 and April 4, 2025.

Author Biographies

  • Sofia Brito, State University of Campinas (UNICAMP)

    Sofia Garcia Telles Brito (sofiagarciatelles@gmail.com) é Bacharela em Matemática Aplicada e Computacional pela Universidade Estadual de Campinas (UNICAMP) e possui interesse em Astronomia, Cosmologia, Aprendizado de Máquina e Processamento de Imagem. (Currículo Lattes: https://lattes.cnpq.br/9686182889855838)

  • Flávia Sobreira, State University of Campinas (UNICAMP)

    Dra. Flavia Sobreira (sobreira@ifi.unicamp.br) é professora e pesquisadora da Unicamp. Trabalha na área de Cosmologia com ênfase na pesquisa sobre a aceleração do universo. (Currículo Lattes: https://lattes.cnpq.br/2432352931378666)

References

[1] K. d. Oliveira, Astronomia e Astrofísica (Editora Livraria da Física, São Paulo, 2014), 3 ed.

[2] D. G. York et al., The sloan digital sky survey: Technical summary, Astronomical Journal 120, 1579 (2000).

[3] B. Abareshi et al., Overview of the instrumentation for the dark energy spectroscopic instrument, Astronomical Journal 164, 207 (2022).

[4] I. Sadeh, F. B. Abdalla e O. Lahav, Annz2 photometric redshift and probability distribution function estimation using machine learning, arXiv:1507.00490 1–22 (2016), university College London, UK; Rhodes University.

[5] Z. Ivezic, A. Connolly, J. VanderPlas e A. Gray, Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton University Press, 2014).

[6] J. A. Newman e D. Gruen, Photometric redshifts for next-generation surveys, arXiv:2206.13633 (2022), disponível em: https://arxiv. org/pdf/2206.13633.pdf.

[7] ROOT Collaboration, Root - data analysis framework, ROOT TMVA Manual (2024), disponível em: https://root.cern/manual/tmva/.

[8] I. Sadeh, Annz2 code and documentation, GitHub repository (2024), disponível em: https://github.com/IftachSadeh/ANNZ.

[9] A. A. Collister e O. Lahav, Annz: Estimating photometric redshifts using artificial neural networks, arXiv preprint astro-ph/0311058 1–6 (2004), university of Cambridge, Cambridge, UK.

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Published

2026-07-30

Issue

Vol. 7 No. Especial (2026): As Astrocientistas: III Encontro Brasileiro de Meninas e Mulheres da Astrofísica, Cosmologia e Gravitação

Section

Resumos de Pôsteres

License

Copyright (c) 2026 Sofia Garcia Telles Brito, Flávia Sobreira

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S. Brito and F. Sobreira, “Estimation of the photometric redshift of galaxies using artificial neural networks”, Cad. Astro., vol. 7, no. Especial, pp. 109–114, Jul. 2026, doi: 10.47456/Cad.Astro.v7nEspecial.53390.