MIST
Magnetosphere, Ionosphere and Solar-Terrestrial
Resolving velocity distribution function parameters from observations with significant Poisson statistical uncertainty
By Georgios Nicolaou (Mullard Space Science Laboratory, UCL)
The interpretation of plasma in-situ measurements often involves the application of standard analysis methods to the observations. Some of these methods adopt simplifications that lead to erroneous results. A recently published study led by Georgios Nicolaou uses simulations of plasma measurements and evaluates the statistical errors of plasma parameters determined by applying three different analysis methods to the data. The study shows that two classic fitting techniques that use chi-squared minimization result in significant systematic misestimations of the plasma parameters when applied to samples with credible statistical uncertainty. On the other hand, the application of the Poisson maximum likelihood method to the same data samples always returns the plasma parameters with negligible systematic errors. The authors quantify the expected errors of the examined methods as functions of the statistical significance of the observations. A follow-up study led by Georgios shows that a classic chi-squared minimization method creates artificial correlations to the determined plasma densities and temperatures, which may be misinterpreted as an actual characteristic behaviour of the examined plasma. However, this is not an issue when the Poisson maximum likelihood method is used to analyse the same observations.
Histograms of plasma bulk parameters determined by applying the three different fit methods to the same measurement samples.
(Left) Plasma density, (middle) temperature, and (right) kappa index, determined by using
Method A: chi-squared minimization with data-driven uncertainty (grey),
Method B: chi-squared minimization with model-driven uncertainty (red), and
Method C: maximum-likelihood method (blue).
The actual plasma parameters (simulation input) are indicated by the vertical magenta line in each panel.
Publications:
Nicolaou, G., Livadiotis, G., Sarlis, N., Ioannou, C. Resolving velocity distribution function parameters from observations with significant Poisson statistical uncertainty, RAS Techniques and Instruments, 2024 3, 874, https://doi.org/10.1093/rasti/rzae059
Nicolaou, G., Livadiotis, G., Ioannou, C. Artificial Polytropic Behavior of Plasmas Determined from the Application of Chi-squared Minimization Analysis to Data with Significant Statistical Uncertainty, The Astrophysical Journal, 2024, 977, 168, 10.3847/1538-4357/ad8f35
Characterising meso-scale ionospheric flow structures
By Gareth Chisham (British Antarctic Survey)
Measuring and understanding ionospheric plasma flow vorticity aids the study of ionospheric plasma transport processes, such as convection and turbulence, which form an important component of magnetosphere to atmosphere space weather models. This plasma flow is dominated by the large-scale convection driven by solar wind-magnetosphere-ionosphere coupling.
This study (https://doi.org/10.1029/2024JA032887) exploits a recently-developed technique that allows the removal of this large-scale component from probability density functions (PDFs) of ionospheric vorticity measured by the Super Dual Auroral Radar Network (SuperDARN). Following this removal, the residual PDFs are symmetric double-sided functions that describe the meso-scale vorticity component that derives from processes below the large scale, such as turbulence. The character of this meso-scale component varies with location in the polar ionosphere, as shown in the figure. The ability to characterise the meso-scale flows in different regions helps to improve our understanding of the meso-scale processes occurring there. Models of ionospheric plasma flow are an important component in larger-scale system models. However, at the present time, these plasma flow models only consider the large-scale convection flow. Understanding, and being able to model, meso-scale ionospheric vorticity will help improve the accuracy of these models.
This figure presents schematic representations of the typical probability density functions (PDFs – f(ω))
of meso-scale ionospheric vorticity (ω) that are observed in different regions of the polar ionosphere:
(a) Dayside cusp; (b) Auroral region; (c) Polar cap; (d) Sub-auroral region.
Publication:
Chisham, G., and Freeman, M.P., The spatial variation of large- and meso-scale plasma flow vorticity statistics in the high-latitude ionosphere and implications for ionospheric plasma flow models. J. Geophys. Res., 129, e2024JA032887, 2024.
https://doi.org/10.1029/2024JA032887
Announcement of New MIST Councillors.
We are very pleased to announce the following members of the community have been elected unopposed to MIST Council:
- Rosie Johnson (Aberystwyth University), MIST Councillor
- Matthew Brown (University of Birmingham), MIST Councillor
- Chiara Lazzeri (MSSL, UCL), Student Representative
Rosie, Matthew, and Chiara will begin their terms in July. This will coincide with Jasmine Kaur Sandhu, Beatriz Sanchez-Cano, and Sophie Maguire outgoing as Councillors.
The current composition of Council can be found on our website, and this will be amended in July to reflect this announcement (https://www.mist.ac.uk/community/mist-council).