Gaussian Process Modelling for Improved Resolution in Faraday Depth Reconstruction

TL;DR

This paper introduces a Gaussian process-based method to infer missing polarization data in radio telescope measurements, enhancing the resolution of Faraday depth spectra and improving the accuracy of rotation measure recovery.

Contribution

The paper presents a novel Gaussian process approach for inferring missing polarization data and directly recovering rotation measure values, improving Faraday depth spectrum resolution.

Findings

Improved Faraday depth spectrum resolution using GP-based data inference.

Accurate recovery of rotation measure values from complex polarization data.

Quantified uncertainties in polarization data regression.

Abstract

The incomplete sampling of data in complex polarization measurements from radio telescopes negatively affects both the rotation measure (RM) transfer function and the Faraday depth spectra derived from these data. Such gaps in polarization data are mostly caused by flagging of radio frequency interference and their effects worsen as the percentage of missing data increases. In this paper we present a novel method for inferring missing polarization data based on Gaussian processes (GPs). Gaussian processes are stochastic processes that enable us to encode prior knowledge in our models. They also provide a comprehensive way of incorporating and quantifying uncertainties in regression modelling. In addition to providing non-parametric model estimates for missing values, we also demonstrate that Gaussian process modelling can be used for recovering rotation measure values directly from complex polarization data, and that inferring missing polarization data using this probabilistic method improves the resolution of reconstructed Faraday depth spectra.