Removing non-physical structure in fitted Faraday rotated signals: non-parametric QU-fitting

TL;DR

This paper introduces a convex non-parametric QU-fitting algorithm that constrains flux at negative wavelengths squared to improve the accuracy of Faraday rotation structure recovery in broadband spectro-polarimetric data.

Contribution

It presents a novel convex non-parametric QU-fitting method that enforces physical constraints, reducing non-physical artifacts in Faraday rotation analysis.

Findings

Lower root mean square error in simulations

Improves accuracy of Faraday structure recovery

Can alter scientific conclusions from real data

Abstract

Next-generation spectro-polarimetric broadband surveys will probe cosmic magnetic fields in unprecedented detail, using the magneto-optical effect known as Faraday rotation. However, non-parametric methods such as RMCLEAN can introduce non-observable linearly polarized flux into a fitted model at negative wavelengths squared. This leads to Faraday rotation structures that are consistent with the observed data, but would be impossible or difficult to measure. We construct a convex non-parametric $QU$-fitting algorithm to constrain the flux at negative wavelengths squared to be zero. This allows the algorithm to recover structures that are limited in complexity to the observable region in wavelength squared. We verify this approach on simulated broadband data sets where we show that it has a lower root mean square error and that it can change the scientific conclusions for real observations. We advise using this prior in next-generation broadband surveys that aim to uncover complex Faraday depth structures. We provide a public Python implementation of the algorithm at \url{https://github.com/Luke-Pratley/Faraday-Dreams}.