Hongjie Zhang (University College London)
Space weather, originating from the Sun, has a profound impact on human life. An effective space weather monitor is crucial for detecting severe space weather events and providing early warnings before they reach Earth. The European Space Agency is currently preparing to launch the Vigil mission as a space-weather monitor at the fifth Lagrange point of the Sun-Earth system. Vigil will carry, amongst other instruments, the Plasma Analyser (PLA) to provide quasi-continuous measurements of solar wind ions.
In this study, we model the performance of the PLA instrument. We employ a forward-modeling technique, which involves predicting measurements (number of particle counts in energy, elevation, and azimuth) based on typical solar wind properties. Then we utilize backward-modeling based on the predicted measurements. This approach allows us to compare the expected observations of the PLA with the assumed input conditions of the solar wind. We evaluate the instrument performance under realistic, non-equilibrium plasma conditions, accounting for temperature anisotropies, proton beams, and the contributions from alpha-particles. We examine the accuracy of the instrument’s performance over a range of input solar wind properties. We recommend potential improvements such as applying ground-based fitting techniques to obtain more accurate measurements of the solar wind even under non-equilibrium plasma conditions. The use of ground processing of plasma moments instead of on-board processing is crucial for the extraction of reliable measurements.
See publication for details:
Zhang, H., Verscharen, D., & Nicolaou, G. (2024). The impact of non-equilibrium plasma distributions on solar wind measurements by Vigil's Plasma Analyser. Space Weather, 22, e2023SW003671. https://doi.org/10.1029/2023SW003671
By Oliver Allanson (University of Birmingham; University of Exeter)
Quasilinear theories have been shown to well describe a range of transport phenomena in magnetospheric, space, astrophysical and laboratory plasma “weak turbulence” scenarios. It is well known that the resonant diffusion quasilinear theory for the case of a uniform background field may formally describe particle dynamics when the electromagnetic wave amplitude and growth rates are sufficiently “small”, and the bandwidth is sufficiently “large”.
However, it is important to note that for a given wave spectrum that would be expected to give rise to quasilinear transport, the theory may apply for a given range of resonant pitch-angles and energies, but may not apply for some smaller, or larger, values of resonant pitch-angle and energy. That is to say that the applicability of the quasilinear theory can be pitch-angle dependent, even in the case of a uniform background magnetic field. If indeed the quasilinear theory does apply, the motion of particles with different pitch-angles are still characterised by different timescales.
Using a high-performance test-particle code, we present a detailed analysis of the applicability of quasilinear theory to a range of different wave spectra that would otherwise “appear quasilinear” if presented by e.g., satellite survey mode data. We present these analyses as a function of wave amplitude, wave coherence and resonant particle velocities (energies and pitch-angles). In doing so, we identify and classify five different transport regimes (see figure) that are a function of particle pitch-angle.
The results in our paper demonstrate that there can be a significant variety of particle responses (as a function of pitch angle) for very similar looking survey-mode electromagnetic wave products, even if they appear to satisfy all appropriate quasilinear criteria.
In recent years there have been a sequence of very interesting and important results in this domain, and we argue in favour of continuing efforts on: (i) the development of new transport theories to understand the importance of these, and other, diverse electron responses; (ii) which are informed by statistical analyses of the relationship between burst- and survey-mode spacecraft data.
For full details see https://doi.org/10.3389/fspas.2024.1332931
By Andy Smith (Northumbria University)
We often have large, unlabelled datasets in space physics, where the phenomenon of interest only appears rarely. Understanding the underlying physics of the system from rare observations is a challenge, and locating complementary, similar observations in large datasets can be prohibitively time consuming.
In this work we present an automated, self-supervised method by which the key information from two dimensional data can be encoded into a smaller vector representation. This representation (encoding/embedding) contains the key information describing the data; we can then use the distance between vectors to assess the similarity of the observations.
We showed the potential of this method with two example datasets – spacecraft in situ electron velocity distributions and auroral all sky images. For both datasets we provided the method with a library of over five thousand images, which were then effectively and automatically summarized by the model.
In the case of the electron distributions, we tested a “seed” image of a rare phenomena – corresponding to the region of space near the site of magnetic reconnection. In this region the electron distribution takes a characteristic crescent or arc-like shape [Figure 1, centre]. We can then extract the six closest partners of this image, using the distance between the embedding vectors. The two closest neighbours of the seed image (A and B in Figure 1) represent two separate previously published case study examples known to be close to the site of magnetic reconnection.
This method promises to be a useful tool in locating interesting phenomena in large datasets, providing an efficient method for moving from case studies to thorough statistical surveys. Code to train an example model is available at: https://github.com/SmithAndy005/SpaceSSL .
See publication for details:
Smith, A. W., Rae, I. J., Stawarz, J. E., Sun, W. J., Bentley, S., & Koul, A. (2024). Automatic encoding of unlabeled two dimensional data enabling similarity searches: Electron diffusion regions and auroral arcs. Journal of Geophysical Research: Space Physics, 129, e2023JA032096. https://doi.org/10.1029/2023JA032096