Fast End-to-End Framework for Cosmological Parameter Inference from CMB Data Using Machine Learning

Abstract

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.