TY - JOUR A1 - Smirnov, Artem A1 - Shprits, Yuri Y. A1 - Allison, Hayley A1 - Aseev, Nikita A1 - Drozdov, Alexander A1 - Kollmann, Peter A1 - Wang, Dedong A1 - Saikin, Anthony T1 - Storm-Time evolution of the Equatorial Electron Pitch Angle Distributions in Earth's Outer Radiation Belt JF - Frontiers in astronomy and space sciences N2 - In this study we analyze the storm-time evolution of equatorial electron pitch angle distributions (PADs) in the outer radiation belt region using observations from the Magnetic Electron Ion Spectrometer (MagEIS) instrument aboard the Van Allen Probes in 2012-2019. The PADs are approximated using a sum of the first, third and fifth sine harmonics. Different combinations of the respective coefficients refer to the main PAD shapes within the outer radiation belt, namely the pancake, flat-top, butterfly and cap PADs. We conduct a superposed epoch analysis of 129 geomagnetic storms and analyze the PAD evolution for day and night MLT sectors. PAD shapes exhibit a strong energy-dependent response. At energies of tens of keV, the PADs exhibit little variation throughout geomagnetic storms. Cap PADs are mainly observed at energies < 300 keV, and their extent in L shrinks with increasing energy. The cap distributions transform into the pancake PADs around the main phase of the storm on the nightside, and then come back to their original shapes during the recovery phase. At higher energies on the dayside, the PADs are mainly pancake during pre-storm conditions and become more anisotropic during the main phase. The quiet-time butterfly PADs can be observed on the nightside at L> 5.6. During the main phase, butterfly PADs have stronger 90 degrees-minima and can be observed at lower L-shells (down to L = 5), then transitioning into flat-top PADs at L similar to 4.5 - 5 and pancake PADs at L < 4.5. The resulting PAD coefficients for different energies, locations and storm epochs can be used to test the wave models and physics-based radiation belt codes in terms of pitch angle distributions. KW - pitch angle KW - pitch angle distributions KW - electrons KW - radiation belts KW - magnetosphere KW - van alien probes Y1 - 2022 U6 - https://doi.org/10.3389/fspas.2022.836811 SN - 2296-987X VL - 9 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Smirnov, Artem A1 - Shprits, Yuri Y. A1 - Allison, Hayley A1 - Aseev, Nikita A1 - Drozdov, Alexander A1 - Kollmann, Peter A1 - Wang, Dedong A1 - Saikin, Anthony T1 - An empirical model of the equatorial electron pitch angle distributions in earth's outer radiation belt JF - Space Weather: the International Journal of Research and Applications N2 - In this study, we present an empirical model of the equatorial electron pitch angle distributions (PADs) in the outer radiation belt based on the full data set collected by the Magnetic Electron Ion Spectrometer (MagEIS) instrument onboard the Van Allen Probes in 2012-2019. The PADs are fitted with a combination of the first, third and fifth sine harmonics. The resulting equation resolves all PAD types found in the outer radiation belt (pancake, flat-top, butterfly and cap PADs) and can be analytically integrated to derive omnidirectional flux. We introduce a two-step modeling procedure that for the first time ensures a continuous dependence on L, magnetic local time and activity, parametrized by the solar wind dynamic pressure. We propose two methods to reconstruct equatorial electron flux using the model. The first approach requires two uni-directional flux observations and is applicable to low-PA data. The second method can be used to reconstruct the full equatorial PADs from a single uni- or omnidirectional measurement at off-equatorial latitudes. The model can be used for converting the long-term data sets of electron fluxes to phase space density in terms of adiabatic invariants, for physics-based modeling in the form of boundary conditions, and for data assimilation purposes. KW - pitch angle KW - radiation belt KW - model KW - magnetosphere KW - van allen probes; KW - electrons Y1 - 2022 U6 - https://doi.org/10.1029/2022SW003053 SN - 1542-7390 VL - 20 IS - 9 PB - American Geophysical Union CY - Washington, DC ER - TY - JOUR A1 - Smirnov, Artem A1 - Berrendorf, Max A1 - Shprits, Yuri Y. A1 - Kronberg, Elena A. A1 - Allison, Hayley J. A1 - Aseev, Nikita A1 - Zhelavskaya, Irina A1 - Morley, Steven K. A1 - Reeves, Geoffrey D. A1 - Carver, Matthew R. A1 - Effenberger, Frederic T1 - Medium energy electron flux in earth's outer radiation belt (MERLIN) BT - a Machine learning model JF - Space weather : the international journal of research and applications N2 - The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis. KW - machine learning KW - radiation belts KW - electron flux KW - empirical modeling KW - magnetosphere KW - electrons Y1 - 2020 U6 - https://doi.org/10.1029/2020SW002532 SN - 1542-7390 VL - 18 IS - 11 PB - American geophysical union, AGU CY - Washington ER -