Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies From Cross‐Phase Spectra

Abstract

The near-Earth environment is composed of different plasma populations that cover an energy range of several orders of magnitude. The plasmasphere is the coldest one (∼1 eV) and typically extends from the top of the ionosphere up to 4-6 Earth radii (R E ). Its shape, composition and extension change in response to geomagnetic activity variation and, due to its preponderant contribution to the mass density, plays a crucial role in exciting plasma-waves and driving their interaction with more energetic particles (Liemohn, 2006;Thorne, 2010). During major storms the outer boundary, the plasmapause, also defined as the plasmasphere boundary layer (PBL, Carpenter & Lemaire, 2004), can move earthward as near as 1.5-2 R E , and the recovery to the pre-storm conditions can last for several days.For its central role in the space weather context, a real-time monitoring of the cold plasma in the inner magnetosphere would be an essential task to achieve. A prototype of such system was developed inside the PLASMON FP7 European project (Lichtenberger et al., 2013) during which the European quasi-Meridional Magnetometer Array (EMMA) was established. EMMA provides a unique opportunity to monitor the plasmasphere dynamics in near real-time. It consists of 27 ground-based magnetic stations approximately located along the same magnetic meridian and mapping into magnetic L-shells in the range 1.6-6.1, where L is the McIlwain parameter evaluated using the IGRF. Using Ultra Low Frequency (ULF) signals simultaneously observed by pairs of stations it is possible to infer the radial distribution of the equatorial plasma mass density in the longitudinal sector identified by the network (Del Corpo et al., 2019;Lichtenberger et al., 2013).The core of this monitoring system is the gradient method proposed by Baransky et al. (1985) and further developed by Waters et al. (1991): By performing Fourier cross-spectral analysis of the magnetic signal recorded by two stations slightly separated in latitude, it is possible to estimate the resonance frequency of the field line whose footprint is halfway between the stations. Around the field line resonance (FLR) frequency

Scaling Laws for the Energy Transfer in Space Plasma Turbulence

“…This field received growing attention in recent years, for its relevant societal applications, with major progresses being made thanks to the implementation of artificial intelligence and machine learning techniques. The latter allowed significant steps forward in the forecasting of solar extreme events (e.g., solar flares and Coronal Mass Ejections, Napoletano et al, 2022) that contribute to inject energy into the heliospheric plasma, reverberating on the dynamics of the near-Earth environment (Camporeale, 2019), of the Earth's magnetosphere (Zhelavskaya et al, 2017;Foldes et al, 2021) and of the solar wind itself (Amaya et al, 2020).…”

Magnetoseismology in a New Era of Magnetospheric Research

“…On the technical side, the process of identifying FLR frequencies in magnetic or electric field data has mostly been performed manually, which is slower than the speed of data accumulation by the spacecraft fleet and networks of ground-based magnetometers, leading to the result that only a small portion of the observed FLR observations has been catalogued and studied. A few efforts have been made to automate this process in ground-based magnetometer data [17,18], in spacecraft data [19], and with machine learning techniques [20]. More efforts in this area are needed to make FLR identification a standard and efficient routine, paving the way to monitor magnetospheric plasma mass density using FLR sounding.…”