TY - JOUR A1 - Shprits, Yuri Y. A1 - Allison, Hayley J. A1 - Wang, Dedong A1 - Drozdov, Alexander A1 - Szabo-Roberts, Matyas A1 - Zhelavskaya, Irina A1 - Vasile, Ruggero T1 - A new population of ultra-relativistic electrons in the outer radiation zone JF - Journal of geophysical research : Space physics N2 - Van Allen Probes measurements revealed the presence of the most unusual structures in the ultra-relativistic radiation belts. Detailed modeling, analysis of pitch angle distributions, analysis of the difference between relativistic and ultra-realistic electron evolution, along with theoretical studies of the scattering and wave growth, all indicate that electromagnetic ion cyclotron (EMIC) waves can produce a very efficient loss of the ultra-relativistic electrons in the heart of the radiation belts. Moreover, a detailed analysis of the profiles of phase space densities provides direct evidence for localized loss by EMIC waves. The evolution of multi-MeV fluxes shows dramatic and very sudden enhancements of electrons for selected storms. Analysis of phase space density profiles reveals that growing peaks at different values of the first invariant are formed at approximately the same radial distance from the Earth and show the sequential formation of the peaks from lower to higher energies, indicating that local energy diffusion is the dominant source of the acceleration from MeV to multi-MeV energies. Further simultaneous analysis of the background density and ultra-relativistic electron fluxes shows that the acceleration to multi-MeV energies only occurs when plasma density is significantly depleted outside of the plasmasphere, which is consistent with the modeling of acceleration due to chorus waves. KW - radiation belts KW - ultra-relativistic electrons KW - EMIC waves KW - modeling; KW - plasma density KW - chorus waves Y1 - 2022 U6 - https://doi.org/10.1029/2021JA030214 SN - 2169-9380 SN - 2169-9402 VL - 127 IS - 5 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Armata, Federico A1 - Vasile, Ruggero A1 - Barcellona, Pablo A1 - Buhmann, Stefan Yoshi A1 - Rizzuto, Lucia A1 - Passante, Roberto T1 - Dynamical Casimir-Polder force between an excited atom and a conducting wall JF - Physical review : A, Atomic, molecular, and optical physics N2 - We consider the dynamical atom-surface Casimir-Polder force in the nonequilibrium configuration of an atom near a perfectly conducting wall, initially prepared in an excited state with the field in its vacuum state. We evaluate the time-dependent Casimir-Polder force on the atom and find that it shows an oscillatory behavior from attractive to repulsive both in time and in space. We also investigate the asymptotic behavior in time of the dynamical force and of related local field quantities, showing that the static value of the force, as obtained by a time-independent approach, is recovered for times much longer than the time scale of the atomic self-dressing but shorter than the atomic decay time. We then discuss the evolution of global quantities such as atomic and field energies and their asymptotic behavior. We also compare our results for the dynamical force on the excited atom with analogous results recently obtained for an initially bare ground-state atom. We show that new relevant features are obtained in the case of an initially excited atom, for example, much larger values of the dynamical force with respect to the static one, allowing for an easier way to single out and observe the dynamical Casimir-Polder effect. Y1 - 2016 U6 - https://doi.org/10.1103/PhysRevA.94.042511 SN - 2469-9926 SN - 2469-9934 VL - 94 SP - 104 EP - 114 PB - American Physical Society CY - College Park ER - TY - JOUR A1 - Liemohn, Michael W. A1 - McCollough, James P. A1 - Jordanova, Vania K. A1 - Ngwira, Chigomezyo M. A1 - Morley, Steven K. A1 - Cid, Consuelo A1 - Tobiska, W. Kent A1 - Wintoft, Peter A1 - Ganushkina, Natalia Yu A1 - Welling, Daniel T. A1 - Bingham, Suzy A1 - Balikhin, Michael A. A1 - Opgenoorth, Hermann J. A1 - Engel, Miles A. A1 - Weigel, Robert S. A1 - Singer, Howard J. A1 - Buresova, Dalia A1 - Bruinsma, Sean A1 - Zhelavskaya, Irina A1 - Shprits, Yuri Y. A1 - Vasile, Ruggero T1 - Model Evaluation Guidelines for Geomagnetic Index Predictions JF - Space Weather: The International Journal of Research and Applications N2 - Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near-Earth space into a single parameter. Most of the best-known indices are calculated from ground-based magnetometer data sets, such as Dst, SYM-H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root-mean-square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices. Plain Language Summary One aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment. Y1 - 2018 U6 - https://doi.org/10.1029/2018SW002067 SN - 1542-7390 VL - 16 IS - 12 SP - 2079 EP - 2102 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Shprits, Yuri Y. A1 - Vasile, Ruggero A1 - Zhelayskaya, Irina S. T1 - Nowcasting and Predicting the Kp Index Using Historical Values and Real-Time Observations JF - Space Weather: The International Journal of Research and Applications N2 - Current algorithms for the real-time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind-based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short-term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer-term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions. KW - Kp index KW - geomagnetic activity KW - empirical prediction KW - solar wind KW - forecast KW - AI Y1 - 2019 U6 - https://doi.org/10.1029/2018SW002141 SN - 1542-7390 VL - 17 IS - 8 SP - 1219 EP - 1229 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Zhelayskaya, Irina S. A1 - Vasile, Ruggero A1 - Shprits, Yuri Y. A1 - Stolle, Claudia A1 - Matzka, Jürgen T1 - Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index JF - Space Weather: The International Journal of Research and Applications N2 - The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance. KW - Kp index KW - Predictive models KW - Feature selection KW - Machine learning KW - Validation Y1 - 2019 U6 - https://doi.org/10.1029/2019SW002271 SN - 1542-7390 VL - 17 IS - 10 SP - 1461 EP - 1486 PB - American Geophysical Union CY - Washington ER -