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
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/279043
Ver los metadatos del registro completo
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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 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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publishedVersion |
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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 |
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http://hdl.handle.net/11336/279043 |
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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 |
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eng |
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eng |
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