KU Leuven Centre for mathematical Plasma-Astrophysics Seminar
Title: Estimating uncertainties in the back-mapping of the fast solar wind
Speaker: Alexandros Koukras from CmPA
Abstract
Despite the fact that the sources of the fast solar wind are known to be the coronal holes, the exact acceleration mechanism that drives the fast solar wind is still not fully understood. An important approach that can improve our understanding is the combination of remote sensing and in situ measurements, which is often referred to as linkage analysis. In order to combine these observables it is necessary to accurately identify the source location of the in situ solar wind with a process called back-mapping.
Typically, back-mapping consists of two main parts, the ballistic mapping from in situ to a point in the outer corona, where the solar wind radially draws the magnetic field into the Parker Spiral and the magnetic mapping, where the solar wind follows the magnetic field line topology down to
the solar surface.
By examining the different sources that can affect the derived back-mapped position of the solar wind, we aim to provide a more precise estimate of the source location. This can then be used to improve the connection of remote sensing with in situ measurements.
For the ballistic mapping we created custom velocity profiles based on the Parker approximations for small and large distances from the Sun. These profiles are constrained by remote observations of the fast solar wind close to the Sun and are used to examine the uncertainty in the ballistic mapping.
The magnetic topology is derived with a potential field source surface extrapolation (PFSS), which takes as input a photospheric synoptic magnetogram. The sensitivity of the extrapolated field in the initial conditions is examined by adding noise to the input magnetogram and performing a Monte Carlo simulation, where for multiple noise realizations we calculate the source position of the solar wind. Next, the effect of free parameters of the framework (like the height of the source surface) is examined and statistical estimates are derived.
Lastly, we use a Gaussian Mixture clustering to provide the optimal grouping of the back-mapped points and an estimate of the uncertainty in the source location. Our uncertainty estimation is compared with other similar frameworks, like the Magnetic Connectivity-Tool of IRAP.
The seminars are in hybrid mode, you can follow in person in room 200B 00.07 or online at the (permanent) link:
https://eu.bbcollab.com/guest/7406a5ec00dc4ec6948200f9c769d454
Date and time: Thursday, February 17, 2022 - 14:00 to 15:00