A New Method of Deriving Doppler Velocities for Solar Orbiter SPICE

TL;DR

This paper introduces a new method for correcting Doppler velocity artifacts in Solar Orbiter SPICE data, improving accuracy by addressing both x and y spectral distortions simultaneously, with publicly available Python tools.

Contribution

A novel correction technique that handles both x and y Doppler artifacts in SPICE data, enhancing the reliability of velocity measurements.

Findings

Significantly improved Doppler velocity signals with less than ~5 km/s uncertainty.

Demonstrated the method on polar observation data, showing orbit-dependent correction parameters.

Provided open-source Python code for community use.

Abstract

This paper presents a follow-up to previous work on correcting PSF-induced Doppler artifacts in observations by the SPICE spectrograph on Solar Orbiter. In a previous paper, we demonstrated correction of these artifacts in the $y-\lambda$ plane with PSF Regularization, treating the forward problem with a method based on large sparse matrix inversion. It has since been found that similar apparent artifacts are also present in the $x-\lambda$ direction, i.e., across adjacent slit positions. This is difficult (although not impossible) to correct with the previous matrix inversion method due to the time variation between slit positions. We have therefore devised a new method which addresses both $x-\lambda$ and $y-\lambda$ artifacts simultaneously by applying wavelength dependent shifts at each $x-y$ plane of the spectral cube. This paper demonstrates the SPICE data issue, describes the new method, and shows a comparison with the previous one. We explore the time variation of the correction parameters for the SPICE data and show a clear orbit dependence. The results of the method are significantly higher quality derived Doppler signals, which we estimate at less than $\sim$ 5 km/s uncertainty for brighter lines in the absence of other systematics. Furthermore, we show the new SPICE polar observation results as a demonstration. The correction codes are written in Python, publicly available on GitHub, and can be directly applied to SPICE level 2 datasets.