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Solar wind back-mapping is a combination of ballistic mapping and magneticmapping. By examining the different model ingredients that can affect thederived back-mapped position, we aim to provide a more precise estimate of thesource location and a measure of confidence in the mapping procedure. This canbe used to improve the connection of remote sensing with in situ measurements.For the ballistic mapping we created custom velocity profiles. These profilesare constrained by observations of the fast solar wind close to the Sun and areused to examine the mapping uncertainty. The coronal magnetic field topologyfrom the solar surface up to the source surface is modeled with a PFSSextrapolation. The sensitivity of the extrapolated field is examined by addingnoise to the input magnetogram and performing a Monte Carlo simulation, wherefor multiple noise realizations we calculate the source position of the solarwind. Next, the effect of free parameters, like the height of the sourcesurface, is examined and statistical estimates are derived. We used GaussianMixture clustering to group the back-mapped points, due to different sources ofuncertainty, and provide a confidence area for the source location of the solarwind. Furthermore, we computed a number of metrics to evaluate the back-mappingresults and assessed their statistical significance by examining 3 high speedstream events. Lastly, we explored the effect of corotation, close to the Sun,on the source region of the solar wind. Our results show that the height of thesource surface produces the largest uncertainty in the source region of thefast solar wind, followed by the choice of the velocity profile and the noisein the input magnetogram. Additionally, we display the ability to derive aconfidence area on the solar surface that represents the potential sourceregion of the in-situ measured fast solar wind.

Estimating uncertainties in the back-mapping of the fast solar wind