Journal Article

Fast End-to-End Framework for Cosmological Parameter Inference from Cosmic Microwave Background Data Using Machine Learning

Santos, Larissa, Baccigalupi, Carlo, Ferreira, Elisa G. M, Novaes, Camila P

2026

Overview

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. In this Letter, we introduce a fast simulation-based end-to-end pipeline combining the Analytical Blind Separation (ABS) method for foreground removal with a neural network (NN) framework for cosmological parameter inference. While both tools are individually established, their integration into a single computationally tractable pipeline has not previously been demonstrated. This is primarily enabled by the exceptional computational efficiency of ABS, which, for the first time, makes large-ensemble simulation-based training feasible in the presence of an explicit component separation step. As a proof of principle under controlled full-sky conditions, we assess this framework for the forthcoming LiteBIRD and PICO satellite missions, obtaining 1σ errors of 0.0035 (LiteBIRD) and 0.0030 (PICO) for the optical depth τ, and 0.0056 (LiteBIRD) and 0.0015 (PICO) for the tensor-to-scalar ratio r. Recovered parameters are consistent with input values within 1σ across most of the test parameter space, with LiteBIRD results consistent with mission forecasts. This demonstrates that the ABS–NN framework provides a computationally efficient and scalable solution for end-to-end CMB inference.

• Publisher: IOP Publishing
• Subject: Computational efficiency / Cosmic microwave background / Cosmology / Galactic emissions / Inference / Machine learning / Neural networks / Optical analysis / Optical thickness / Parameter estimation / Parameters / Physics / Radiation / Separation / Simulation / Tensors / Universe