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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01291 |
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| _version_ | 1866910095690956800 |
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| author | Santos, Larissa Novaes, Camila P. Ferreira, Elisa G. M. Baccigalupi, Carlo |
| author_facet | Santos, Larissa Novaes, Camila P. Ferreira, Elisa G. M. Baccigalupi, Carlo |
| contents | Precise estimation of cosmological parameters from the cosmic microwave background (CMB) remains a central goal of modern cosmology and a key test of inflationary physics. However, this task is fundamentally limited by strong foreground contamination, primarily from Galactic emissions, which obscure the faint CMB B-mode polarization signal. In this Letter, we introduce a fast, simulation-based, end to end pipeline that integrates a robust component separation technique with machine-learning, leading to cosmological parameter estimation. Our approach combines the Analytical Blind Separation (ABS) method for foreground removal with a neural network (NN) framework optimized to extract cosmological parameters directly from full-sky simulations. We assess the performance of this methodology for the forthcoming LiteBIRD and PICO satellite missions, designed to detect CMB B modes with unprecedented sensitivity. Applying the pipeline to realistic sky simulations, we obtain 1 sigma errors of 0.0035 (LiteBIRD) and 0.0030 (PICO) for the optical depth tau, and 0.005 (LiteBIRD) and 0.0014 (PICO) for the tensor-to-scalar ratio, r. In all cases, the recovered parameters are consistent with input values within 1 sigma across most of the parameter space. Results for LiteBIRD are in excellent agreement with the latest forecasts from the collaboration. Our findings establish this combined ABS-NN pipeline as a competitive, accurate, and computationally efficient alternative for cosmological parameter inference, offering a powerful framework for forthcoming CMB experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast End-to-End Framework for Cosmological Parameter Inference from CMB Data Using Machine Learning Santos, Larissa Novaes, Camila P. Ferreira, Elisa G. M. Baccigalupi, Carlo Cosmology and Nongalactic Astrophysics Precise estimation of cosmological parameters from the cosmic microwave background (CMB) remains a central goal of modern cosmology and a key test of inflationary physics. However, this task is fundamentally limited by strong foreground contamination, primarily from Galactic emissions, which obscure the faint CMB B-mode polarization signal. In this Letter, we introduce a fast, simulation-based, end to end pipeline that integrates a robust component separation technique with machine-learning, leading to cosmological parameter estimation. Our approach combines the Analytical Blind Separation (ABS) method for foreground removal with a neural network (NN) framework optimized to extract cosmological parameters directly from full-sky simulations. We assess the performance of this methodology for the forthcoming LiteBIRD and PICO satellite missions, designed to detect CMB B modes with unprecedented sensitivity. Applying the pipeline to realistic sky simulations, we obtain 1 sigma errors of 0.0035 (LiteBIRD) and 0.0030 (PICO) for the optical depth tau, and 0.005 (LiteBIRD) and 0.0014 (PICO) for the tensor-to-scalar ratio, r. In all cases, the recovered parameters are consistent with input values within 1 sigma across most of the parameter space. Results for LiteBIRD are in excellent agreement with the latest forecasts from the collaboration. Our findings establish this combined ABS-NN pipeline as a competitive, accurate, and computationally efficient alternative for cosmological parameter inference, offering a powerful framework for forthcoming CMB experiments. |
| title | Fast End-to-End Framework for Cosmological Parameter Inference from CMB Data Using Machine Learning |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2511.01291 |