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
By Daniel Ratliff (Northumbria University)
Whistler-Mode Chorus (WMC) waves remain a key contributor to the processes underpinning space weather modelling and have garnered considerable interest for their unique frequency properties (known as tones, where the frequency will rise or fall coherently). This role and phenomena are in no small part due to the interplay between these waves and the electrons present in the magnetosphere. At present, these wave particle interactions are difficult to model simultaneously effectively, and we normally restrict ourselves to the effect of one on the other – either a known wave is used to develop a particle distribution, or a supplied particle distribution generates WMC waves. Can we develop models that do both? And furthermore, can we develop a model that can reproduce this interesting set of frequency dynamics?
In our paper, we use formal perturbation techniques to derive a reduced, nonlinear model for (parallel propagating) WMC that is driven by wave-particle interactions via ponderomotive effects. Our first attempt, the famous Nonlinear Schrodinger equation, fails to generate tones – and so we dig a little deeper to find a term responsive for nonlinear frequency shifts. Surprisingly, this new term responsible for tones vanishes precisely at the WMC band gap at half the electron gyrofrequency, and provides a theoretical basis for why such a bandgap exists. By exploring this model numerically, we also find that there are cases where this tonal behaviour comes with a significant enhancement of the electron kinetic energy – so maybe the magnetosphere’s dawn chorus is at times a swan song in disguise?
See publication for further information:
Ratliff DJ, Allanson O. The nonlinear evolution of whistler-mode chorus: modulation instability as the source of tones. Journal of Plasma Physics. 2023;89(6):905890607. doi:10.1017/S0022377823001265
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