DeepWiener: neural networks for CMB polarization maps and power spectrum computation

Autores
Costanza, María Belén; Scoccola, Claudia Graciela; Zaldarriaga, Matías
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
To study the early Universe, it is essential to estimate cosmological parameters with high accuracy, which depends on the optimal reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of their power spectrum. In this paper, we generalize the neural network developed for applying the Wiener Filter, initially presented for temperature maps in previous work, to polarization maps. Our neural network has a UNet architecture, including an extra channel for the noise variance map, to account for inhomogeneous noise, and a channel for the mask. In addition, we propose an iterative approach for reconstructing the E and B-mode fields, while addressing the E-to-B leakage present in the maps due to incomplete sky coverage. The accuracy achieved is satisfactory compared to the Wiener Filter solution computed with the standard Conjugate Gradient method, and it is highly efficient, enabling the computation of the power spectrum of an unknown signal using the optimal quadratic estimator. We further evaluate the quality of the reconstructed maps at the power spectrum level along with their corresponding errors, finding that these errors are smaller than those obtained using the well-known pseudo-C ℓ approach. Our results show that increasing complexity in the applied mask presents a more significant challenge for B-mode reconstruction.
Fil: Costanza, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Scoccola, Claudia Graciela. Universidad de Chile. Facultad de Ciencias Físicas y Matemáticas; Chile. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Zaldarriaga, Matías. No especifíca;
Materia
CMBR experiments
CMBR polarisation
Machine learning
Cosmology and Nongalactic Astrophysics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/279043

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spelling DeepWiener: neural networks for CMB polarization maps and power spectrum computationCostanza, María BelénScoccola, Claudia GracielaZaldarriaga, MatíasCMBR experimentsCMBR polarisationMachine learningCosmology and Nongalactic Astrophysicshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1To study the early Universe, it is essential to estimate cosmological parameters with high accuracy, which depends on the optimal reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of their power spectrum. In this paper, we generalize the neural network developed for applying the Wiener Filter, initially presented for temperature maps in previous work, to polarization maps. Our neural network has a UNet architecture, including an extra channel for the noise variance map, to account for inhomogeneous noise, and a channel for the mask. In addition, we propose an iterative approach for reconstructing the E and B-mode fields, while addressing the E-to-B leakage present in the maps due to incomplete sky coverage. The accuracy achieved is satisfactory compared to the Wiener Filter solution computed with the standard Conjugate Gradient method, and it is highly efficient, enabling the computation of the power spectrum of an unknown signal using the optimal quadratic estimator. We further evaluate the quality of the reconstructed maps at the power spectrum level along with their corresponding errors, finding that these errors are smaller than those obtained using the well-known pseudo-C ℓ approach. Our results show that increasing complexity in the applied mask presents a more significant challenge for B-mode reconstruction.Fil: Costanza, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Scoccola, Claudia Graciela. Universidad de Chile. Facultad de Ciencias Físicas y Matemáticas; Chile. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Zaldarriaga, Matías. No especifíca;IOP Publishing2025-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/279043Costanza, María Belén; Scoccola, Claudia Graciela; Zaldarriaga, Matías; DeepWiener: neural networks for CMB polarization maps and power spectrum computation; IOP Publishing; Journal of Cosmology and Astroparticle Physics; 2025; 05; 5-2025; 1-331475-7516CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1475-7516/2025/05/058info:eu-repo/semantics/altIdentifier/doi/10.1088/1475-7516/2025/05/058info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-26T10:03:31Zoai:ri.conicet.gov.ar:11336/279043instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-02-26 10:03:31.375CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv DeepWiener: neural networks for CMB polarization maps and power spectrum computation
title DeepWiener: neural networks for CMB polarization maps and power spectrum computation
spellingShingle DeepWiener: neural networks for CMB polarization maps and power spectrum computation
Costanza, María Belén
CMBR experiments
CMBR polarisation
Machine learning
Cosmology and Nongalactic Astrophysics
title_short DeepWiener: neural networks for CMB polarization maps and power spectrum computation
title_full DeepWiener: neural networks for CMB polarization maps and power spectrum computation
title_fullStr DeepWiener: neural networks for CMB polarization maps and power spectrum computation
title_full_unstemmed DeepWiener: neural networks for CMB polarization maps and power spectrum computation
title_sort DeepWiener: neural networks for CMB polarization maps and power spectrum computation
dc.creator.none.fl_str_mv Costanza, María Belén
Scoccola, Claudia Graciela
Zaldarriaga, Matías
author Costanza, María Belén
author_facet Costanza, María Belén
Scoccola, Claudia Graciela
Zaldarriaga, Matías
author_role author
author2 Scoccola, Claudia Graciela
Zaldarriaga, Matías
author2_role author
author
dc.subject.none.fl_str_mv CMBR experiments
CMBR polarisation
Machine learning
Cosmology and Nongalactic Astrophysics
topic CMBR experiments
CMBR polarisation
Machine learning
Cosmology and Nongalactic Astrophysics
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv To study the early Universe, it is essential to estimate cosmological parameters with high accuracy, which depends on the optimal reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of their power spectrum. In this paper, we generalize the neural network developed for applying the Wiener Filter, initially presented for temperature maps in previous work, to polarization maps. Our neural network has a UNet architecture, including an extra channel for the noise variance map, to account for inhomogeneous noise, and a channel for the mask. In addition, we propose an iterative approach for reconstructing the E and B-mode fields, while addressing the E-to-B leakage present in the maps due to incomplete sky coverage. The accuracy achieved is satisfactory compared to the Wiener Filter solution computed with the standard Conjugate Gradient method, and it is highly efficient, enabling the computation of the power spectrum of an unknown signal using the optimal quadratic estimator. We further evaluate the quality of the reconstructed maps at the power spectrum level along with their corresponding errors, finding that these errors are smaller than those obtained using the well-known pseudo-C ℓ approach. Our results show that increasing complexity in the applied mask presents a more significant challenge for B-mode reconstruction.
Fil: Costanza, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Scoccola, Claudia Graciela. Universidad de Chile. Facultad de Ciencias Físicas y Matemáticas; Chile. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Zaldarriaga, Matías. No especifíca;
description To study the early Universe, it is essential to estimate cosmological parameters with high accuracy, which depends on the optimal reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of their power spectrum. In this paper, we generalize the neural network developed for applying the Wiener Filter, initially presented for temperature maps in previous work, to polarization maps. Our neural network has a UNet architecture, including an extra channel for the noise variance map, to account for inhomogeneous noise, and a channel for the mask. In addition, we propose an iterative approach for reconstructing the E and B-mode fields, while addressing the E-to-B leakage present in the maps due to incomplete sky coverage. The accuracy achieved is satisfactory compared to the Wiener Filter solution computed with the standard Conjugate Gradient method, and it is highly efficient, enabling the computation of the power spectrum of an unknown signal using the optimal quadratic estimator. We further evaluate the quality of the reconstructed maps at the power spectrum level along with their corresponding errors, finding that these errors are smaller than those obtained using the well-known pseudo-C ℓ approach. Our results show that increasing complexity in the applied mask presents a more significant challenge for B-mode reconstruction.
publishDate 2025
dc.date.none.fl_str_mv 2025-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/279043
Costanza, María Belén; Scoccola, Claudia Graciela; Zaldarriaga, Matías; DeepWiener: neural networks for CMB polarization maps and power spectrum computation; IOP Publishing; Journal of Cosmology and Astroparticle Physics; 2025; 05; 5-2025; 1-33
1475-7516
CONICET Digital
CONICET
url http://hdl.handle.net/11336/279043
identifier_str_mv Costanza, María Belén; Scoccola, Claudia Graciela; Zaldarriaga, Matías; DeepWiener: neural networks for CMB polarization maps and power spectrum computation; IOP Publishing; Journal of Cosmology and Astroparticle Physics; 2025; 05; 5-2025; 1-33
1475-7516
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1475-7516/2025/05/058
info:eu-repo/semantics/altIdentifier/doi/10.1088/1475-7516/2025/05/058
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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