Aliza Mustafa | Searcharxiv

arXiv2026

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

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

astro-ph.CO↗

arXiv2026

Blind mitigation of foreground-induced biases on primordial $B$ modes for ground-based CMB experiments

Observations of the Cosmic Microwave Background (CMB) B-mode polarisation provide a unique probe of inflationary physics. Extracting a reliable constraint on the tensor-to-scalar ratio $r$ nonetheless demands stringent suppression of diffuse Galactic foregrounds, whose residuals can bias the inferred signal. This work introduces and evaluates two extensions of the Needlet Internal Linear Combination (NILC) framework aimed at reducing foreground-induced biases on $r$. The first extension implements the deprojection of selected foreground moments directly within the component-separation step. The second performs a likelihood-level marginalisation over residual foreground power using a data-driven template. Using Simons Observatory Small Aperture Telescope (SO-SAT) - like simulations, we show that both methods effectively control residual contamination, yielding unbiased estimates of $r$ and a consistent reconstruction of the lensing B-mode amplitude. These results indicate that enhanced foreground-mitigation strategies will be useful for next-generation CMB polarisation analyses seeking a robust detection of primordial B-modes.

astro-ph.CO↗