With nonperturbative laser-induced fluorescence measurements of ion flow, we confirm numerical simulations of spontaneous electric double-layer (DL) formation in a current-free expanding plasma. Measurements in two different experiments confirm that the DL is localized to the region of rapidly diverging magnetic field. The measurements indicate that the trapped ion population is a single Maxwellian, that the spatial gradient of the energy of ions accelerated through the DL matches the magnetic field gradient, and that DL formation is triggered when the ion-neutral collisional mean-free path exceeds the magnetic field gradient scale length.

Dawn–dusk asymmetries are ubiquitous features of the coupled solar-wind–magnetosphere–ionosphere system. During the last decades, increasing availability of satellite and ground-based measurements has made it possible to study these phenomena in more detail. Numerous publications have documented the existence of persistent asymmetries in processes, properties and topology of plasma structures in various regions of geospace. In this paper, we present a review of our present knowledge of some of the most pronounced dawn–dusk asymmetries. We focus on four key aspects: (1) the role of external influences such as the solar wind and its interaction with the Earth's magnetosphere; (2) properties of the magnetosphere itself; (3) the role of the ionosphere and (4) feedback and coupling between regions. We have also identified potential inconsistencies and gaps in our understanding of dawn–dusk asymmetries in the Earth's magnetosphere and ionosphere.

Experimental data are presented that are consistent with the hypothesis that anomalous rf absorption in helicon sources is due to electron scattering arising from parametrically driven ion-acoustic waves downstream from the antenna. Also presented are ion temperature measurements demonstrating anisotropic heating $(T_{\perp }>T_{||})$ at the edge of the discharge. The most likely explanation is ion-Landau damping of electrostatic slow waves at a local lower-hybrid-frequency resonance.

Recently, we demonstrated that a single, tunable, low-power, diode laser can be used for laser-induced fluorescence (LIF) measurements of both argon ions and helium neutrals. We have now identified a third fluorescence scheme, for neutral argon atoms, accessible with the same tunable diode laser. Fluorescence measurements of a heated iodine cell are used to monitor the wavelength of the laser during the LIF measurement.

With the availability of data and computational technologies in the modern world, machine learning (ML) has emerged as a preferred methodology for data analysis and prediction. While ML holds great promise, the results from such models are not fully unreliable due to the challenges introduced by uncertainty. An ML model generates an optimal solution based on its training data. However, if the uncertainty in the data and the model parameters are not considered, such optimal solutions have a high risk of failure in actual world deployment. This paper surveys the different approaches used in ML to quantify uncertainty. The paper also exhibits the implications of quantifying uncertainty when using ML by performing two case studies with space physics in focus. The first case study consists of the classification of auroral images in predefined labels. In the second case study, the horizontal component of the perturbed magnetic field measured at the Earth’s surface was predicted for the study of Geomagnetically Induced Currents (GICs) by training the model using time series data. In both cases, a Bayesian Neural Network (BNN) was trained to generate predictions, along with epistemic and aleatoric uncertainties. Finally, the pros and cons of both Gaussian Process Regression (GPR) models and Bayesian Deep Learning (DL) are weighed. The paper also provides recommendations for the models that need exploration, focusing on space weather prediction.