BROOM: a python package for model-independent analysis of microwave astronomical data

TL;DR

BROOM is a Python package designed for model-independent analysis and component separation of microwave astronomical data, supporting both known and unknown spectral signals, with tools for diagnostics and simulation.

Contribution

It introduces a comprehensive Python toolkit for blind and non-blind component separation, diagnostics, and simulations tailored for microwave astronomy.

Findings

Validated pipelines for satellite and ground-based experiments.

Demonstrated effective separation of known spectral components.

Provided tools for residual contamination estimation.

Abstract

We present BROOM, a new python package for the application of blind, minimum-variance component-separation techniques to microwave observations. The package enables the reconstruction of signals with known spectral energy distributions, such as the Cosmic Microwave Background (CMB), Sunyaev--Zeldovich distortions, or foreground moments, in both temperature and polarization through a suite of Internal Linear Combination (ILC) implementations, in the presence of astrophysical and instrumental contaminants. In addition, BROOM supports the blind reconstruction of coherent emission components with unknown covariance properties via a Generalized ILC (GILC) framework. Beyond component separation, the package provides tools to diagnose foreground complexity and to estimate residual contamination leaking into reconstructed maps across angular scales and sky regions. It also includes utilities to generate realistic microwave simulations for arbitrary CMB experiments and to compute angular power spectra of the resulting products. We present a comprehensive description and validation of the implemented pipelines in two representative experimental configurations: a full-sky satellite mission and a ground-based experiment. BROOM is publicly available, fully documented, and easily installable at https://github.com/alecarones/broom