GPU-Accelerated JAX Framework Boosts CMB Polarization Analysis

This paper introduces a novel, JAX-powered implementation of a parametric component-separation method for CMB polarization data, specifically designed to address the challenges posed by spatially varying foreground Spectral Energy Distributions (SEDs). The framework, built within the FURAX environment, extends the fgbuster parametric formalism and enables fully vectorized, GPU-accelerated evaluation of the spectral likelihood, map reconstruction, and diagnostic metrics across tens of thousands of pixel subset configurations, noise realizations, and sky regions. The implementation achieves significant performance gains, with up to ~100x speed-up over the scipy TNC optimizer when running on GPUs. When applied to LiteBIRD-like simulations, the optimized K-means configuration reduces the 68% upper limit on the tensor-to-scalar ratio r by approximately 30% relative to a fixed, previously derived multi-resolution configuration, while maintaining competitive statistical uncertainties. This research represents a significant advancement in CMB data analysis, enabling more robust and efficient extraction of cosmological information from complex datasets.

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