CosmoForge I: A unified framework for QML power spectrum estimation and pixel-based likelihood analysis

TL;DR

CosmoForge is a comprehensive Python framework that unifies QML power spectrum estimation and pixel-based likelihood analysis for CMB data, optimizing computations and ensuring consistency with established methods.

Contribution

It introduces a modular, validated, and extensible framework combining QML estimation and pixel likelihood within a single, efficient Python package.

Findings

Reproduces Planck low-ell reference implementation accurately

Reduces Fisher matrix computation complexity to O(ell_max^4)

Provides a flexible, multi-normalization analysis pipeline

Abstract

Optimal power spectrum estimation on the largest angular scales of the cosmic microwave background relies on the Quadratic Maximum Likelihood (QML) estimator. Existing public implementations, however, each address only a subset of the problem and none combine power spectrum estimation with a self-consistent pixel-space likelihood within a single framework. We present CosmoForge, a public Python framework that unifies QML power spectrum estimation and pixel-based Gaussian likelihood evaluation for spin-0 and spin-2 fields on the sphere, with general (non-diagonal) noise covariances. The framework is split into three installable packages: CosmoCore (infrastructure), QUBE (Fisher and QML estimation), and PICSLike (pixel-space likelihood). A common interface exposes two interchangeable computation bases $-$ a harmonic basis built on the Sherman-Morrison-Woodbury identity and a direct pixel-space basis $-$ selecting whichever is cheaper for the configuration at hand. Exact algorithmic optimisations reduce the Fisher cost to $\mathcal{O}(\ell_{\rm max}^4)$ for arbitrary noise covariances, with Numba JIT compilation of the hot kernels and MPI parallelisation of the likelihood scan. CosmoForge reproduces the Planck low-$\ell$ Fortran reference implementation across both the QML and pixel-space likelihood pipelines, consistently with double-precision arithmetic. Native multipole binning and three output normalisations (deconvolved, decorrelated, window-convolved) are exposed through a single code path, and the same covariance infrastructure powers both QML estimation and likelihood evaluation. CosmoForge offers a general-purpose, modular, and validated tool for the optimal analysis of large-scale data on the sphere. It is publicly available, pip-installable, and extensible to non-CMB observables.