Statistics of Solar Wind Electron Breakpoint Energies Using Machine Learning Techniques

Solar wind electron velocity distributions at 1 au consist of a thermal "core" population and two suprathermal populations: "halo" and "strahl". The core and halo are quasi-isotropic, whereas the strahl typically travels radially outwards along the parallel and/or anti-parallel direction with respect to the interplanetary magnetic field. With Cluster-PEACE data, we analyse energy and pitch angle distributions and use machine learning techniques to provide robust classifications of these solar wind populations. Initially, we use unsupervised algorithms to classify halo and strahl differential energy flux distributions to allow us to calculate relative number densities, which are of the same order as previous results. Subsequently, we apply unsupervised algorithms to phase space density distributions over ten years to study the variation of halo and strahl breakpoint energies with solar wind parameters. In our statistical study, we find both halo and strahl suprathermal breakpoint energies display a significant increase with core temperature, with the halo exhibiting a more positive correlation than the strahl. We conclude low energy strahl electrons are scattering into the core at perpendicular pitch angles. This increases the number of Coulomb collisions and extends the perpendicular core population to higher energies, resulting in a larger difference between halo and strahl breakpoint energies at higher core temperatures. Statistically, the locations of both suprathermal breakpoint energies decrease with increasing solar wind speed. In the case of halo breakpoint energy, we observe two distinct profiles above and below 500 km/s. We relate this to the difference in origin of fast and slow solar wind.Comment: Published in Astronomy & Astrophysics, 11 pages, 10 figure

Identifying the magnetotail lobes with Cluster magnetometer data

We describe a novel method for identifying times when a spacecraft is in Earth’s magnetotail lobes solely using magnetometer data. We propose that lobe intervals can be well identified as times when the magnetic field is strong and relatively invariant, defined using thresholds in the magnitude of BX and the standard deviation σ of the magnetic field magnitude. Using data from the Cluster spacecraft at downtail distances greater than 8 RE during 2001–2009, we find that thresholds of 30 nT and 3.5 nT, respectively, optimize agreement with a previous, independently derived lobe identification method that used both magnetic and plasma data over the same interval. Specifically, our method has a moderately high accuracy (66%) and a low probability of false detection (11%) in comparison to the other method. Furthermore, our method identifies the lobe on many other occasions when the previous method was unable to make any identification and yields longer continuous intervals in the lobe than the previous method, with intervals at the 90th percentile being triple the length. Our method also allows for analyses of the lobes outside the time span of the previous method

On the Relative Strength of Electric and Magnetic ULF Wave Radial Diffusion During the March 2015 Geomagnetic Storm

In this paper, we study electron radial diffusion coefficients derived from Pc4‐Pc5 ultralow frequency (ULF) wave power during the intense geomagnetic storm on 17–18 March 2015. During this storm the population of highly relativistic electrons was depleted within 2 hr of the storm commencement. This radial diffusion, depending upon the availability of source populations, can cause outward radial diffusion of particles and their loss to the magnetosheath, or inward transport and acceleration. Analysis of electromagnetic field measurements from Geostationary Operational Environment Satellite (GOES), Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellite, and ground‐based magnetometers shows that the main phase storm‐specific radial diffusion coefficients do not correspond to statistical estimates. Specifically, during the main phase, the electric diffusion ( urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0001) is reduced, and the magnetic diffusion ( urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0002) is increased, compared to empirical models based on Kp. Contrary to prior results, the main phase magnetic radial diffusion cannot be neglected. The largest discrepancies, and periods of dominance of urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0003 over urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0004, occur during intervals of strongly southward IMF. However, during storm recovery, both magnetic and electric diffusion rates are consistent with empirical estimates. We further verify observationally, for the first time, an energy coherence for both urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0005 and urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0006 where diffusion coefficients do not depend on energy. We show that, at least for this storm, properly characterizing main phase radial diffusion, potentially associated with enhanced ULF wave magnetopause shadowing losses, cannot be done with standard empirical models. Modifications, associated especially with southward IMF, which enhance the effects of urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0007 and introduce larger main phase outward transport losses, are needed

The influence of substorms on extreme rates of change of the surface horizontal magnetic field in the United Kingdom

We investigate how statistical properties of the rate of change R of the surface horizontal magnetic field in the United Kingdom differ during substorm expansion and recovery phases compared with other times. R is calculated from 1‐min magnetic field data from three INTERMAGNET observatories—Lerwick, Eskdalemuir, and Hartland and between 1996 and 2014—nearly two solar cycles. Substorm expansion and recovery phases are identified from the SuperMAG Lower index using the Substorm Onsets and Phases from Indices of the Electrojet method. The probability distribution of R is decomposed into categories of whether during substorm expansion and recovery phases, in enhanced convection intervals, or at other times. From this, we find that 54–56% of all extreme R values (defined as above the 99.97th percentile) occur during substorm expansion or recovery phases. By similarly decomposing the magnetic local time variation of the occurrence of large R values (>99th percentile), we deduce that 21–25% of large R during substorm expansion and recovery phases are attributable to the Disturbance Polar (DP)1 magnetic perturbation caused by the substorm current wedge. This corresponds to 10–14% of all large R in the entire data set. These results, together with asymptotic trends in occurrence probabilities, may indicate the two‐cell DP2 magnetic perturbation caused by magnetospheric convection as the dominant source of hazardous R > 600 nT/min that is potentially damaging to the U.K. National Grid. Thus, further research is needed to understand and model DP2, its mesoscale turbulent structure, and substorm feedbacks in order that GIC impact on the National Grid may be better understood and predicted

Accurately characterising the importance of wave-particle interactions in radiation belt dynamics: the pitfalls of statistical wave representations

Wave-particle interactions play a crucial role in energetic particle dynamics in the Earth's radiation belts. However the relative importance of different wave-modes in these dynamics is poorly understood. Typically this is assessed during geomagnetic storms using statistically averaged empirical wave models as a function of geomagnetic activity in advanced radiation belt simulations. However statistical averages poorly characterise extreme events such as geomagnetic storms in that storm-time ULF wave power is typically larger than that derived over a solar cycle and Kp is a poor proxy for storm-time wave power

Variability of quasilinear diffusion coefficients for plasmaspheric hiss

In the Outer Radiation Belt, the acceleration and loss of high-energy electrons is largely controlled by wave-particle interactions. Quasilinear diffusion coefficients are an efficient way to capture the small-scale physics of wave-particle interactions due to magnetospheric wave modes such as plasmaspheric hiss. The strength of quasilinear diffusion coefficients as a function of energy and pitch-angle depends on both wave parameters and plasma parameters such as ambient magnetic field strength, plasma number density and composition. For plasmaspheric hiss in the magnetosphere, observations indicate large variations in the wave intensity and wavenormal angle, but less is known about the simultaneous variability of the magnetic field and number density. We use in-situ measurements from the Van Allen Probe mission to demonstrate the variability of selected factors that control the size and shape of pitch-angle diffusion coefficients: wave intensity, magnetic field strength and electron number density. We then compare with the variability of diffusion coefficients calculated individually from co-located and simultaneous groups of measurements. We show that the distribution of the plasmaspheric hiss diffusion coefficients is highly non-Gaussian with large variance, and that the distributions themselves vary strongly across the three phase-space bins studied. In most bins studied, the plasmaspheric hiss diffusion coefficients tend to increase with geomagnetic activity, but our results indicate that new approaches that include natural variability may yield improved parameterizations. We suggest methods like stochastic parameterization of wave-particle interactions could use variability information to improve modelling of the Outer Radiation Belt

Automated determination of auroral breakup during the substorm expansion phase using all sky imager data

This technique paper describes a novel method for quantitatively and routinely identifying auroral breakup following substorm onset using the Time History of Events and Macroscale Interactions During Substorms (THEMIS) all-sky imagers (ASIs). Substorm onset is characterised by a brightening of the aurora that is followed by auroral poleward expansion and auroral breakup. This breakup can be identified by a sharp increase in the auroral intensity i(t) and the time derivative of auroral intensity i'(t). Utilising both i(t) and i'(t) we have developed an algorithm for identifying the time interval and spatial location of auroral breakup during the substorm expansion phase within the field of view of ASI data based solely on quantifiable characteristics of the optical auroral emissions. We compare the time interval determined by the algorithm to independently identified auroral onset times from three previously published studies. In each case the time interval determined by the algorithm is within error of the onset independently identified by the prior studies. We further show the utility of the algorithm by comparing the breakup intervals determined using the automated algorithm to an independent list of substorm onset times. We demonstrate that up to 50% of the breakup intervals characterised by the algorithm are within the uncertainty of the times identified in the independent list. The quantitative description and routine identification of an interval of auroral brightening during the substorm expansion phase provides a foundation for unbiased statistical analysis of the aurora to probe the physics of the auroral substorm as a new scientific tool for aiding the identification of the processes leading to auroral substorm onset

Statistical characterisation of the growth and spatial scales of the substorm onset arc

We present the first multi-event study of the spatial and temporal structuring of the aurora to provide statistical evidence of the near-Earth plasma instability which causes the substorm onset arc. Using data from ground-based auroral imagers, we study repeatable signatures of along-arc auroral beads, which are thought to represent the ionospheric projection of magnetospheric instability in the near-Earth plasma sheet. We show that the growth and spatial scales of these wave-like fluctuations are similar across multiple events, indicating that each sudden auroral brightening has a common explanation. We find statistically that growth rates for auroral beads peak at low wavenumber with the most unstable spatial scales mapping to an azimuthal wavelength λ≈1700 − 2500 km in the equatorial magnetosphere at around 9-12 RE. We compare growth rates and spatial scales with a range of theoretical predictions of magnetotail instabilities, including the cross-field current instability and the shear-flow ballooning instability. We conclude that, although the cross-field current instability can generate similar magnitude of growth rates, the range of unstable wavenumbers indicates that the shear-flow ballooning instability is the most likely explanation for our observations