Accurate and efficient likelihood modeling for large-scale CMB data

TL;DR

This paper introduces a new likelihood modeling method for large-scale CMB data that improves accuracy and efficiency by marginalizing over auto-spectra, validated through simulations and benchmarking against existing methods.

Contribution

A novel likelihood modeling approach based on the Hamimeche-Lewis formalism that reduces biases and outperforms existing methods in accuracy while maintaining computational efficiency.

Findings

Outperforms existing likelihood methods in accuracy.

Reduces residual biases from noise and sky coverage.

Maintains computational efficiency for large datasets.

Abstract

Accurate parameter estimation from cosmic microwave background data requires reliable likelihood modeling, particularly at large angular scales where angular power spectrum estimators exhibit non-Gaussian statistics. We present a novel approach, based on the Hamimeche-Lewis formalism, that marginalizes over auto-spectra, thus reducing residual biases from noise misestimation and partial sky coverage. We validate our approach by simulating three independent CMB channels, or data splits, in a multi-field setting, comparing to the pixel-based likelihood ground truth estimates for the optical depth $\tau$ and the tensor-to-scalar ratio $r$. We benchmark our method against the main power spectrum based alternatives available in the literature, showing that it outperforms all of them in terms of accuracy, while remaining fast and computationally efficient.