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Register a new userMachine-learning based discovery of missing physical processes in radiation belt modeling
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Real-time prediction of the dynamics of energetic electrons in Earths radiation belts incorporating incomplete observation data is important to protect valuable artificial satellites and to understand their physical processes. Traditionally, reduced models have employed a diffusion equation based on the quasilinear approximation. Using a Physics-Informed Neutral Network (PINN) framework, we train and test a model based on four years of Van Allen Probe data. We present a recipe for gleaning physical insight from solving the ill-posed inverse problem of inferring model coefficients from data using PINNs. With this, it is discovered that the dynamics of killer electrons is described more accurately instead by a drift-diffusion equation. A parameterization for the diffusion and drift coefficients, which is both simpler and more accurate than existing models, is presented.
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Here we present the recent progress in upgrading a predictive model for Megaelectron-Volt (MeV) electrons inside the Earths outer Van Allen belt. This updated model, called PreMevE 2.0, is demonstrated to make much improved forecasts, particularly at outer Lshells, by including upstream solar wind speeds to the models input parameter list. Furthermore, based on several kinds of linear and artificial machine learning algorithms, a list of models were constructed, trained, validated and tested with 42-month MeV electron observations from Van Allen Probes. Out-of-sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1-day (2-day) forecasts with Lshell-averaged performance efficiency values ~ 0.87 (~0.82). Interestingly, the linear regression model is often the most successful one when compared to other models, which indicates the relationship between 1 MeV electron dynamics and precipitating electrons is dominated by linear components. It is also shown that PreMevE 2.0 can reasonably predict the onsets of MeV electron events in 2-day forecasts. This improved PreMevE model is driven by observations from longstanding space infrastructure (a NOAA satellite in low-Earth-orbit, the solar wind monitor at the L1 point, and one LANL satellite in geosynchronous orbit) to make high-fidelity forecasts for MeV electrons, and thus can be an invaluable space weather forecasting tool for the future.
We report on computer simulations and analytic theory to provide a self-consistent understanding of the role of the reconnection electric field, which extends substantially beyond the simple change of magnetic connections. Rather, we find that the reconnection electric field is essential to maintaining the current density in the diffusion region, which would otherwise be dissipated by a set of processes. Natural candidates for current dissipation are the average convection of current carriers away from the reconnection region by the outflow of accelerated particles, or the average rotation of the current density by the magnetic field reversal in the vicinity. Instead, we show here that the current dissipation is the result of thermal effects, underlying the statistical interaction of current-carrying particles with the adjacent magnetic field. We find that this interaction serves to redirect the directed acceleration of the reconnection electric field to thermal motion. This thermalization manifests itself in form of quasi-viscous terms in the thermal energy balance of the current layer. These quasi-viscous terms act to increase the average thermal energy. Our predictions regarding current and thermal energy balance are readily amenable to exploration in the laboratory or by satellite missions, in particular, by NASAs Magnetospheric Multiscale mission.
The transverse stability of the target is crucial for obtaining high quality ion beams using the laser radiation pressure acceleration (RPA) mechanism. In this letter, a theoretical model and supporting two-dimensional (2D) Particle-in-Cell (PIC) simulations are presented to clarify the physical mechanism of the transverse instability observed in the RPA process. It is shown that the density ripples of the target foil are mainly induced by the coupling between the transverse oscillating electrons and the quasi-static ions, a mechanism similar to the transverse two stream instability in the inertial confinement fusion (ICF) research. The predictions of the mode structure and the growth rates from the theory agree well with the results obtained from the PIC simulations in various regimes, indicating the model contains the essence of the underlying physics of the transverse break-up of the target.
Two types of approaches to modeling molecular systems have demonstrated high practical efficiency. Density functional theory (DFT), the most widely used quantum chemical method, is a physical approach predicting energies and electron densities of molecules. Recently, numerous papers on machine learning (ML) of molecular properties have also been published. ML models greatly outperform DFT in terms of computational costs, and may even reach comparable accuracy, but they are missing physicality - a direct link to Quantum Physics - which limits their applicability. Here, we propose an approach that combines the strong sides of DFT and ML, namely, physicality and low computational cost. By generalizing the famous Hohenberg-Kohn theorems, we derive general equations for exact electron densities and energies that can naturally guide applications of ML in Quantum Chemistry. Based on these equations, we build a deep neural network that can compute electron densities and energies of a wide range of organic molecules not only much faster, but also closer to exact physical values than curre
Fast reverse shocks (FRSs) cause the magnetosphere to expand, by contrast to the well-known compressions caused by the impact of fast forward shocks (FFS). Usually, FFSs are more geoeffective than FRSs, and consequently, the inner magnetosphere dynamic responses to both shock types can be quite different. In this study, we investigate for the first time the radiation belt response to an FRS impact using multi-satellite observations and numerical simulations. Spacecraft on the dayside observed decreases in magnetic field strength and energetic (~ 40-475 keV) particle fluxes. Timing analysis shows that the magnetic field signature propagated from the dayside to the nightside magnetosphere. Particles with different energies vary simultaneously at each spacecraft, implying a non-dispersive particle response to the shock. Spacecraft located at lower L-shells did not record any significant signatures. The observations indicate a local time dependence of the response associated with the shock inclination, with the clearest signatures being observed in the dusk-midnight sector. Simulations underestimate the amplitude of the magnetic field variations observed on the nightside. The observed decreases in the electron intensities result from a combination of radial gradient and adiabatic effects. The radial gradients in the spectral index appear to be the dominant contributor to the observed variations of electrons seen on the dayside (near noon and dusk) and on the nightside (near midnight). This study shows that even an FRS can affect the radiation belts significantly and provides an opportunity to understand their dynamic response to a sudden expansion of the magnetosphere.
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