BeyondPlanck X. Planck LFI frequency maps with sample-based error propagation

TL;DR

This paper introduces a new Bayesian framework for deriving Planck LFI frequency maps that enables comprehensive error propagation and systematic uncertainty modeling, significantly improving calibration accuracy and providing detailed covariance information.

Contribution

The paper presents a novel sample-based Bayesian approach for Planck LFI map-making, allowing full propagation of instrumental uncertainties and improved calibration precision compared to previous methods.

Findings

Lower calibration uncertainties at 70 GHz by a factor of 40.

Supports full Bayesian error propagation for instrumental effects.

Enables direct production of low-resolution data products with covariance matrices.

Abstract

We present Planck LFI frequency sky maps derived within the BeyondPlanck framework. This framework draws samples from a global posterior distribution that includes instrumental, astrophysical and cosmological parameters, and the main product is an entire ensemble of frequency sky map samples. This ensemble allows for computationally convenient end-to-end propagation of low-level instrumental uncertainties into higher-level science products. We show that the two dominant sources of LFI instrumental systematic uncertainties are correlated noise and gain fluctuations, and the products presented here support - for the first time - full Bayesian error propagation for these effects at full angular resolution. We compare our posterior mean maps with traditional frequency maps delivered by the Planck collaboration, and find generally good agreement. The most important quality improvement is due to significantly lower calibration uncertainties in the new processing, as we find a fractional absolute calibration uncertainty at 70 GHz of $\delta g_{0}/g_{0} =5 \cdot 10^{-5}$, which is nominally 40 times smaller than that reported by Planck 2018. However, the original Planck 2018 estimate has a non-trivial statistical interpretation, and this further illustrates the advantage of the new framework in terms of producing self-consistent and well-defined error estimates of all involved quantities without the need of ad hoc uncertainty contributions. We describe how low-resolution data products, including dense pixel-pixel covariance matrices, may be produced directly from the posterior samples without the need for computationally expensive analytic calculations or simulations. We conclude that posterior-based frequency map sampling provides unique capabilities in terms of low-level systematics modelling and error propagation, and may play an important role for future CMB B-mode experiments. (Abridged.)