The PICASSO map-making code: application to a simulation of the QUIJOTE northern sky survey

TL;DR

This paper introduces PICASSO, a new map-making code based on destriping, designed for CMB polarization experiments like QUIJOTE, demonstrating high-fidelity reconstruction of simulated northern sky survey data with minimal signal error.

Contribution

The paper presents PICASSO, a destriping-based map-making algorithm tailored for ground-based CMB polarization observations, validated through realistic simulations of the QUIJOTE survey.

Findings

PICASSO accurately reconstructs the injected CMB signal with high fidelity.

The signal error remains below 0.001% for multipoles 20<ℓ<200.

The method effectively detects CMB anisotropies via cross-correlation analysis.

Abstract

Map-making is an important step for the data analysis of Cosmic Microwave Background (CMB) experiments. It consists of converting the data, which are typically a long, complex and noisy collection of measurements, into a map, which is an image of the observed sky. We present in this paper a new map-making code named PICASSO (Polarization and Intensity CArtographer for Scanned Sky Observations), which was implemented to construct intensity and polarization maps from the Multi Frequency Instrument (MFI) of the QUIJOTE (Q-U-I Joint TEnerife) CMB polarization experiment. PICASSO is based on the destriping algorithm, and is suited to address specific issues of ground-based microwave observations, with a technique that allows the fit of a template function in the time domain, during the map-making step. This paper describes the PICASSO code, validating it with simulations and assessing its performance. For this purpose, we produced realistic simulations of the QUIJOTE-MFI survey of the northern sky (approximately $\sim 20,000\,$deg$^2$), and analysed the reconstructed maps with PICASSO, using real and harmonic space statistics. We show that, for this sky area, PICASSO is able to reconstruct, with high fidelity, the injected signal, recovering all the scales with $\ell>10$ in TT, EE and BB. The signal error is better than 0.001% at $20<\ell<200$. Finally, we validated some of the methods that will be applied to the real wide-survey data, like the detection of the CMB anisotropies via cross-correlation analyses. Despite that the implementation of PICASSO is specific for QUIJOTE-MFI data, it could be adapted to other experiments.