Electron Flux Data Research Articles

Comparative Analysis of TPA‐LSTM and Transformer Models for Forecasting GEO Radiation Belt Electron Fluxes

AbstractThe geosynchronous orbit (GEO) is a region filled with energetic electrons and it hosts hundreds of satellites. Electron fluxes at GEO can change sharply within hours, making high‐time‐resolution prediction crucial. In this study, we develop and compare two neural networks for persistent high‐time‐resolution prediction: long short‐term memory with temporal pattern attention (TPA‐LSTM) and Transformer. Unlike most previous models, which only output electron fluxes, our models output the same parameters as the inputs, including magnetic local time, solar wind speed, solar wind dynamic pressure, AE, Kp, Dst, the north‐south component of the interplanetary magnetic field, and electron flux data from GOES‐15. The models are trained on approximately six years of data (2012–2016) and validated using about one year of data (2017–2018). We compare the TPA‐LSTM and Transformer models using 0.8 MeV electron fluxes and find that while the Transformer model performs slightly better, the difference is not statistically significant. Considering the Transformer's higher computational cost, we use the TPA‐LSTM model to develop prediction models for electron fluxes of 275, 475, 0.8 MeV, and 2 MeV with a 5‐min resolution at GEO, up to 3 days. The prediction efficiencies (PE) for 275, 475, 0.8 and 2 MeV electron fluxes based on about one year of test data (2018–2019) are 0.799, 0.831, 0.849, 0.881 (1‐day prediction); and 0.551, 0.618, 0.663 and 0.710 (3‐day prediction), respectively. Our high‐time‐resolution persistent models should be useful for both protecting satellites at GEO and serving as boundary conditions for physics‐based radiation belt models.

Empirical Model of SSUSI‐Derived Auroral Ionization Rates

AbstractWe present an empirical model for auroral (90–150 km) electron–ion pair production rates, ionization rates for short, derived from Special Sensor Ultraviolet Spectrographic Imager electron energy and flux data. Using the Fang et al. (2010, https://doi.org/10.1029/2010gl045406) parametrization for mono‐energetic electrons, and the NRLMSISE‐00 neutral atmosphere model (Picone et al., 2002, https://doi.org/10.1029/2002ja009430), the calculated ionization rate profiles are binned in 2‐hr magnetic local time and 3.6°geomagnetic latitude to yield time series of ionization rates at 5‐km altitude steps. We fit each of these time series to the geomagnetic indices Kp, PC, and Ap, the 81‐day averaged solar radio flux index, and a constant term. The resulting empirical model can easily be incorporated into coupled chemistry–climate models to include particle precipitation effects.

The Evolution of Earth's Outer Radiation Belt Over Geomagnetic Storm Phase in Van Allen Probe Era

AbstractEarth's outer radiation belts are highly dynamic during geomagnetic storms. Using electron flux data from 226 keV to 2.6 MeV measured by the Van Allen Probes, we statistically analyzed the peak flux position and inner boundary position of the outer radiation belt across different storm phases: pre‐storm quiet time, main phase, early recovery phase, and later recovery phase. This analysis covered 196 geomagnetic storm events from October 2012 to September 2019. Our results indicate that: (a) During the pre‐storm, decreases with increasing energy. From the pre‐storm to the early recovery phase, shifts inward for energies below 1 MeV and outward for energies above 1 MeV. For all energies, converges to approximately L = 4.3–4.6 in the early recovery phase. (b) Below approximately 1 MeV, generally move inward from the main phase to the early recovery phase. Above 1 MeV, remains nearly unchanged across different storm phases. (c) The half‐width (−) of the outer belt decreases during the main phase for energies below 1 MeV and increases during the recovery phase for energies above 1.5 MeV. (d) In the early recovery phase, and at 593–742 keV show a moderate correlation with storm intensity (CC 0.7–0.8), while and at energies greater than 1.1 MeV exhibit low correlations (CC0.4) during each phase. These results confirm the complex, energy‐dependent morphology of the outer radiation belt throughout geomagnetic storm phases.

The Relationship Between Electron Precipitation and the Population of Trapped Electrons in LEO: New Evidence Supporting a Natural Limit to the Flux of Energetic Electrons

AbstractEnergetic particle precipitation into the atmosphere has been identified as a key loss process for electrons in the Earth's outer radiation belt region. However, direct measurements of the electron flux precipitating into the atmosphere are challenging from high altitude low inclination spacecraft, such as the Van Allen Probes, due to the small angular size of the loss cone along the orbital path of these high‐altitude spacecraft. Here we use data from the Polar Orbiting Environmental Satellites (POES)/Space Environment Monitor in low‐Earth orbit to assess the relationship between the trapped and precipitating electron flux in integral energy channels >30, >100, and >300 keV. Our results highlight that there is a strong non‐linear relationship between the flux of trapped and precipitating electrons, with the ratio of precipitating to trapped flux only becoming significantly enhanced once the trapped flux reaches a critical level. This transition from low to high levels of precipitation is consistent with the theory proposed by Kennel and Petschek (K‐P) (1966) https://doi.org/10.1029/JZ071i001p00001, whereby intense chorus waves are excited and trigger pitch angle diffusion, resulting in energetic particle precipitation and limiting the trapped flux. Using electron flux data from POES and chorus wave data from the Van Allen Probes, we further test the observations against predictions from the K‐P theory. A particle tracing model is also utilized to illustrate a direct link between the drift paths of injected electrons, the occurrence of chorus waves and the spatial distribution of strong electron precipitation into the atmosphere at different energies.

Prediction of Radiation Belts Electron Fluxes at a Low Earth Orbit Using Neural Networks With PROBA‐V/EPT Data

AbstractWe introduce for the first time the PROBA‐V/EPT electron flux data to train a deep learning data‐driven model with the purpose of investigating the Earth’s radiation belts dynamics. The Long‐Short Term Memory Neural Network is employed to predict the electron fluxes between 1 and 8 Earth Radius (RE) along a Low Earth Orbit. Different combinations of time series inputs involving Solar Wind and geomagnetic data are tested, based on previous knowledge of their impact onto the high energy radiation fluxes. Two Energetic Particle Telescope energy channels feed the learning procedure for nonrelativistic (0.5–0.6 MeV) and relativistic (1.0–2.4 MeV) electron fluxes. A good performance of the model employing different time resolutions from hours to days is demonstrated with a correlation of more than 0.9 between the predicted and out‐of‐sample fluxes, and a prediction efficiency that can attain between 0.6 and 0.9 depending on the L range. The analysis of different input parameters and time resolutions allows to construct the best data set structure and improve the model to identify relevant effects such as dropouts, flux increase and recovery features.

Performance evaluation of SNB3GEO electrons flux forecasting model using LANL and GOES-13 observations

Energetic electrons in the outer radiation belt can damage the spacecrafts that operate within the region, such as geostationary satellites. For the purpose of terrestrial application, communications, broadcasting and metreological forecast, the existence of geostationary satellites are extremely useful. To provide alerts on the variations and enhancements of relativistic electron fluxes that can damage the spacecrafts, models which predict the relativistic electrons flux need to be accurate enough. In forecasting relativistic electron fluxes, SNB3GEO model provides relatively reliable and easily interpretable prediction output. Although the performance of this model was evaluated with respect to other models (such as Relativistic Electron Forecast Model (REFM)), its prediction accuracy never assessed with respect to experimental measurements except those used in model development (i.e. GOES-12, GOES-13). In this paper, we implemented evaluation of the prediction performance of SNB3GEO using two experimental measurements, which are LANL and GOES-13. The daily averaged relativistic electron flux data from 01 January 2013 to 31 December 2016 have been used to analyze the forecasting performance. Correlation Coefficient (CC), Normalized Root Mean Square Error (NRMSE) and Prediction Efficiency (PE) have been used as performance evaluation quantifier. The results indicated that SNB3GEO provides a more accurate forecast when it is compared with GOES-13 than LANL observations. The four years average values of CC,NRMSE and PE, when the model performance has been evaluated with observations of GOES-13 are 0.896,0.497 and 0.792, respectively, whereas the average values are CC=0.739,NRMSE=0.706 and PE=0.450, when the model performance evaluated with respect to observations of LANL. Such prediction performance evaluation of SNB3GEO will contribute for further improvement of the model used to predict energetic electrons fluxes in Earth’s outer radiation belt.

Multi‐Event Analysis of the Correlation Between Chorus Waves and Electron Butterfly Distribution Using Van Allen Probes Observation

AbstractIn this study, using Van Allen Probes observations we identify 81 events of electron flux bursts with butterfly pitch angle distributions for tens of keV electrons with close correlations with chorus wave bursts in the Earth's magnetosphere. We use the high‐rate electron flux data from Magnetic Electron Ion Spectrometer available during 2013–2019 and the simultaneous whistler‐mode wave measurements from Electric and Magnetic Field Instrument Suite and Integrated Science to identify the correlated events. The events are categorized into 67 upper‐band chorus (0.5–0.8 fce) dominated events and 14 other events where lower‐band chorus (0.05–0.5 fce) has modest or strong amplitudes (fce represents electron cyclotron frequency). Each electron flux burst correlated with chorus has a short timescale of ∼1 min or less, suggesting potential nonlinear effects. The statistical distribution of selected electron burst events tends to occur in the post‐midnight sector at L > 5 under disturbed geomagnetic conditions, and is associated with chorus waves with relatively strong magnetic wave amplitude and small wave normal angle. The frequency dependence of the electron flux peaks agrees with the cyclotron resonant condition, indicating the effects of chorus‐induced electron acceleration. Our study provides new insights into understanding the rapid nonlinear interactions between chorus and energetic electrons.

Dependence of radiation belt flux depletions at geostationary orbit on different solar drivers during intense geomagnetic storms

The loss of electron flux of the outer radiation belt has been widely studied in terms of the mechanism that brings in these losses. There are a few studies which have attempted to explain the interplanetary conditions that favor the depletions. As the Sun is the prime cause of any change happening in the magnetosphere, it is important to look at the solar drivers that bring in such changes. In this study, we attempt to understand the effect of solar structures and substructures on the loss of radiation belt high-energy electrons during intense geomagnetic storms. The superposed epoch analysis is used to observe any peculiar changes in GOES electron flux data during the storms that are associated with solar structures such as CME and CIR, ICME substructures such as the magnetic cloud, magnetic cloud with sheath, ejecta, ejecta with sheath, and only sheath. The long-term data also give an opportunity to compare the flux decrease during solar cycles 23 and 24. It has been observed that 1) CIR-associated storms cause a comparatively higher flux decrease than CME-associated storms, 2) sheath-related storms bring out a higher flux decrease, and 3) there is no significant change in flux for the storms of both the solar cycles. The flux decrease in intense storms at the geostationary orbit is essentially triggered by the “Dst effect.” Apart from this, the minimum IMF Bz and northward IMF Bz before turning southward add to the flux decrease. These results hold true for the electron depletions occurring only during intense geomagnetic storms and may alter otherwise.

72‐Hour Time Series Forecasting of Hourly Relativistic Electron Fluxes at Geostationary Orbit by Deep Learning

AbstractIn this study, we forecast hourly relativistic (>2 MeV) electron fluxes at geostationary orbit for the next 72 hr using a deep learning model based on multilayer perceptron. The input data of the model are solar wind parameters (temperature, density and speed), interplanetary magnetic field (|B| and Bz), geomagnetic indices (Kp and Dst), and electron fluxes themselves. All input data are hourly averaged ones for the preceding 72 consecutive hours. We use electron flux data from Geostationary Operational Environmental Satellite (GOES)‐15 and ‐16, and perform a mapping for matching these two data. Total period of the data is from 2011 January to 2021 March (GOES‐15 data for 2011–2017 and GOES‐16 data for 2018–2021). We divide the data into training set (January–August), validation set (September), and test set (October–December) to consider the solar cycle effect. Our main results are as follows. First, our model successfully predicts hourly electron fluxes for the next 72 hr. Second, root‐mean‐square error of our model is from 0.18 (for 1 hr prediction) to 0.68 (for 72 hr prediction), and prediction efficiency is from 0.97 to 0.53, which are much better than those of the previous studies. Third, our model well predicts both diurnal variation and sudden increases of electron fluxes associated with fast solar winds and interplanetary magnetic fields. Our study implies that the deep learning model can be applied to forecasting long‐term sequential space weather events.

An on-orbit cross-calibration between the relativistic electron observations from BeiDou M04 and GPS ns63

Understanding the radiation belts comprehensively has prominent significance both for aerospace engineering and space weather. For both the development of radiation belts models and the research into the dynamics of energetic electrons in the inner magnetosphere, the availability of global, well cross-calibrated data is crucial. In this report, we carry out an on-orbit cross-calibration between the observations from the High Energy Electron Detector (HEED) on Bei Dou navigation satellite system (BD) of China and Combined X-ray and Dosimeter (CXD) on Global Positioning System (GPS) constellation using the relativistic electron flux data from 2012 to 2014 and obtain the systematic offsets between the two systems. The fitting results show that there is a good consistency with the electron observations from M04 and ns63 with linear fitting slopes between relativistic electron fluxes from HEED and CXD all near 0.9. Using the cross-calibration results, the observations of greater than 1 MeV electrons of M04-HEED and GPS-CXD are assimilated well by removing the system deviation. Taking advantage of assimilated electron observations from M04 and ns63, the evolution of electron energy spectrums during the two sequential magnetic storms in March 2013 is investigated. Different dynamic properties presented by assimilated observations suggest different physical process dominant during these storms. Furthermore, this case study demonstrates the significance of the cross-calibration between M04 and ns64 observations and the usefulness of assimilated observations. This work is the first try to show that the relativistic electron observation from BD is compared with the same type operational satellite GPS. It could be expected that huge assimilated observations using the cross-calibration presented in this work would be extremely useful for statistical radiation belt modeling research, space weather nowcasting and forecasting, and comprehensive scientific understanding of energetic electron dynamic processes in the radiation belts.

Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning

AbstractWe present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV ∼1 MeV in the outer radiation belt. The Outer Radiation belt Electron Neural net model for Medium energy electrons uses only solar wind conditions and geomagnetic indices as input. The models are trained on electron flux data from the Magnetic Electron Ion Spectrometer instrument onboard Van Allen Probes, and they can reproduce the dynamic variations of electron fluxes in different energy channels. The model results show high coefficient of determination (R2 ∼ 0.78–0.92) on the test data set, an out‐of‐sample 30‐day period from 25 February to 25 March in 2017, when a geomagnetic storm took place, as well as an out‐of‐sample one year period after March 2018. In addition, the models are able to capture electron dynamics such as intensifications, decays, dropouts, and the Magnetic Local Time dependence of the lower energy (∼<100 keV) electron fluxes during storms. The models have reliable prediction capability and can be used for a wide range of space weather applications. The general framework of building our model is not limited to radiation belt fluxes and could be used to build machine learning models for a variety of other plasma parameters in the Earth's magnetosphere.

Modeling the dynamic variability of sub-relativistic outer radiation belt electron fluxes using machine learning

We present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV $\sim$ 1 MeV in the outer radiation belt. The models take satellite position, a time-history of geomagnetic indices, and solar wind conditions in different length time windows as inputs. The models are then trained on electron flux data from the Magnetic Electron Ion Spectrometer (MagEIS) instrument onboard Van Allen Probes, and they can reproduce the dynamic variations of electron fluxes in different energy channels. The model results show high coefficient of determination (R-squared $\sim$ 0.78-0.92) on the test dataset, an out-of-sample 30-day period from February 25 to March 25 in 2017, when a geomagnetic storm took place, as well as an out-of-sample one-year period after March 2018. In addition, the models are able to capture electron dynamics such as intensifications, decays, dropouts, and the Magnetic Local Time (MLT) dependence of the lower energy ($\sim <$ 100 keV )electron fluxes during storms. The models have reliable prediction capability and can be used for a wide range of space weather applications. The general framework of building our model is not limited to radiation belt fluxes and could be used to build machine learning models for a variety of other plasma parameters in the Earth's magnetosphere.

On the interpretation of the latest AMS-02 cosmic ray electron spectrum

The latest AMS-02 data on cosmic ray electrons show a break in the energy spectrum around 40 GeV, with a change in the slope of about 0.1. We perform a combined fit to the newest AMS-02 positron and electron flux data above 10 GeV using a semi-analytical diffusion model where sources includes production of pairs from pulsar wind nebulae (PWNe), electrons from supernova remnants (SNRs) and both species from spallation of hadronic cosmic rays with interstellar medium atoms. We demonstrate that within our setup the change of slope in the AMS-02 electron data is well explained by the interplay between the flux contributions from SNRs and from PWNe. In fact, the relative contribution to the data of these two populations changes by a factor of about 13 from 10 to 1000 GeV. The PWN contribution has a significance of at least 4σ, depending on the model used for the propagation, interstellar radiation field and energy losses. We checked the stability of this result against low-energy effects by solving numerically the transport equation, as well as adding possible breaks in the injection spectrum of SNRs. The effect of the energy losses alone, when the inverse Compton scattering is properly computed within a fully numerical treatment of the Klein-Nishina cross section, cannot explain the break in the e− flux data, as recently proposed in the literature.

Study of an Equatorward Detachment of Auroral Arc From the Oval Using Ground‐Space Observations and the BATS‐R‐US–CIMI Model

AbstractWe present observations of an equatorward detachment of the auroral arc from the main oval and magnetically conjugate measurements made by the Arase satellite in the inner magnetosphere. The all‐sky imager at Gakona (magnetic latitude = 63.6°N), Alaska, shows the detachment of the auroral arc in both red and green lines at local midnight (∼0130–0230 MLT) on 30 March 2017. The electron density derived from the Arase in‐situ observations shows that this arc occurred outside the plasmapause. At the arc crossing, the electron flux of energies ∼0.1–2 keV is found to be locally enhanced at L∼4.3–4.5. We estimated auroral intensities for both red and green lines by using the Arase low‐energy (0.1–19 keV) electron flux data. The peak latitude of the estimated intensity shows reasonably good correspondence with the observed intensity mapped at the ionospheric footprints of the Arase satellite. These findings indicate that the observed arc detachment at Gakona was associated with the localized enhancement of low‐energy electrons (∼0.1–2 keV) at the inner edge of the electron plasma sheet. Further, we employ the simulation results of the Community Coordinated Modeling Center (CCMC), the BATS‐R‐US–CIMI 3‐D MHD code to understand the conditions in the inner magnetosphere around the time of detachment. Although the simulation could not reproduce the lower‐energy component responsible for the arc detachment, it successfully reproduced two earthward convection events at the lower radial distance (R) (R ≤ ∼4) around the time of arc detachment and the features of enhanced convection in similarity with the observations.

Novel interpretation of the latest AMS-02 cosmic-ray electron spectrum

The latest AMS-02 data on cosmic ray electrons show a break in the energy spectrum around 40 GeV, with a change in the slope of about 0.1. We perform a combined fit to the newest AMS-02 positron and electron flux data above 10 GeV using a semi-analytical diffusion model where sources includes production of pairs from pulsar wind nebulae (PWNe), electrons from supernova remnants (SNRs) and both species from spallation of hadronic cosmic rays with interstellar medium atoms. We demonstrate that within our setup the change of slope in the AMS-02 electron data is well explained by the interplay between the flux contributions from SNRs and from PWNe. In fact, the relative contribution to the data of these two populations changes by a factor of about 13 from 10 to 1000 GeV. The PWN contribution has a significance of at least $4\sigma$, depending on the model used for the propagation, interstellar radiation field and energy losses. We checked the stability of this result against low-energy effects by solving numerically the transport equation. as well as adding possible breaks in the injection spectrum of SNRs. The effect of the energy losses alone, when the inverse Compton scattering is properly computed within a fully numerical treatment of the Klein-Nishina cross section, cannot explain the break in the $e^-$ flux data, as recently proposed in the literature.

Constraining the Location of the Outer Boundary of Earth's Outer Radiation Belt

AbstractCharacterizing the location of the outer boundary of the outer radiation belt is a key aspect of improving radiation belt models and helps to constrain our understanding of the mechanisms by which the source and seed electron populations are transported into the radiation belts. In this paper, we hypothesize that there are statistical differences in the electron distribution function across the radiation belt outer boundary, and thus analyze electron flux data from the THEMIS (Time History of Events and Macroscale Interactions during Substorms) satellites to identify this location. We validate our hypothesis by using modeled electron L* values to approximately characterize the differences between electron distribution functions inside and outside of the radiation belts. Initially, we perform a simple statistical analysis by studying the radial evolution of the electron distribution functions. This approach does not yield a clear discontinuity, thus highlighting the need for more complex statistical treatment of the data. Subsequently, we employ machine learning (with no dependence on radial position or L*) to test a range of candidate outer boundary locations. By analyzing the performance of the models at each candidate location, we identify a statistical boundary at ≈8 RE, with results suggesting some variability. This statistical boundary is typically further out than those used in current radiation belt models.

Double‐Peak Structures of Martian Nightside Total Electron Content in Strong Crustal Magnetic Cusp Regions

AbstractWith the help of the high‐spatial‐resolution total electron content (TEC) data obtained by Mars Advanced Radar for Subsurface and Ionospheric Sounding on board the Mars Express (MEx) during June 2005 to September 2007, the detailed configurations of the Martian nightside ionosphere in crustal magnetic field cusp regions are researched in this paper. It is found that double‐peak structures of orbital observed TEC values frequently appeared when MEx was crossing through the strong crustal magnetic field cusp regions. This observational result implies that the Martian nightside TECs in the periphery of the cusp regions are generally higher than those in the central areas of the cusp regions, a phenomenon never reported before. By combining the TEC data with the in‐situ electron flux data obtained by the Analyzer of Space Plasma and Energetic Atoms‐3 (ASPERA‐3), it is found that the difference between the fluxes of the electrons precipitating in the central and peripheral areas of the cusp regions can be responsible for the observed TEC double‐peak structures.

RBSP‐ECT Combined Pitch Angle Resolved Electron Flux Data Product

AbstractWe describe a new data product combining pitch angle resolved electron flux measurements from the Radiation Belt Storm Probes (RBSP) Energetic Particle Composition and Thermal Plasma (ECT) suite on the National Aeronautics and Space Administration’s Van Allen Probes. We describe the methodology used to combine each of the data sets and produce a consistent set of pitch‐angle‐resolved spectra for the entire Van Allen Probes mission. Three‐minute‐averaged flux spectra are provided spanning energies from 15 eV up to 20 MeV. This new data product offers researchers a consistent cross calibrated data set to explore the particle dynamics of the inner magnetosphere across a wide range of energies.

Observations of Particle Loss due to Injection‐Associated Electromagnetic Ion Cyclotron Waves

AbstractWe report on observations of electromagnetic ion cyclotron (EMIC) waves and their interactions with injected ring current particles and high energy radiation belt electrons. The magnetic field experiment aboard the twin Van Allen Probes spacecraft measured EMIC waves near L = 5.5–6. Particle data from the spacecraft show that the waves were associated with particle injections. The wave activity was also observed by a ground‐based magnetometer near the spacecraft geomagnetic footprint over a more extensive temporal range. Phase space density profiles, calculated from directional differential electron flux data from Van Allen Probes, show that there was a significant energy‐dependent relativistic electron dropout over a limited L‐shell range during and after the EMIC wave activity. In addition, the NOAA spacecraft observed relativistic electron precipitation associated with the EMIC waves near the footprint of the Van Allen Probes spacecraft. The observations suggest EMIC wave‐induced relativistic electron loss in the radiation belt.

Wavelet and Cross-Correlation Analysis of Relativistic Electron Flux with Sunspot Number, Solar Flux, and Solar Wind Parameters

The Geostationary Operational Environmental Satellites (GOES) have been monitoring the Earth's radiation environment and is providing the electron flux data (of energy &gt;0.8 MeV, &gt;2 MeV, and &gt;4 MeV) by means of a connected sensor subsystem. Relativistic electron flux is one of the components of the radiation belt which not only affects the electrical system in satellites but also has an impact on Earth’s upper atmospheric climatic variation. We have carried out a study to determine the relation of sunspot number (R), solar flux (F10.7), and solar wind parameters i.e., solar wind velocity (Vsw), plasma density Nsw), the southern component of the interplanetary magnetic field (IMF-Bz), Plasma temperature (Tsw) with relativistic electron flux of energy &gt;0.8 MeV, &gt;2 MeV, and &gt;4 MeV in outer radiation belt using the data of 24 years (1996-2020) covering solar cycle 23 and 24. Time series analysis, Cross-correlation and wavelet analysis techniques have been used in this study. The time series plot displayed that the radiation is occupied mostly by electron flux of energy less than 4 Mev and solar cycle 23 (1996-2008) was strong to produce more intensity of relativistic electron flux of all energy in comparison to cycle 24 (2008-2019). Results from cross-correlation analysis illustrated that Bz has no significant impact on the enhancement of relativistic electron flux of any energy range in the radiation belt. Whereas other studied parameters have a positive correlation with relativistic electron flux, but with significantly different coefficient values for different energy. We found that electron flux &gt;0.8 MeV and &gt;2 MeV has a strong positive association with sunspot number, solar flux, solar wind velocity, plasma density and temperature whereas weak correlation with electron flux of energy &gt;4 MeV. This result leads us to conclude that solar activity and solar parameters have greater influence in producing relativistic electron flux of energy ~ 0.8-4 MeV, than of flux &gt; 4 MeV. The study made to observe the distribution of relativistic electrons in radiation belt with time through continuous wavelet analysis showed that electron flux of energy &gt;0.8 has a higher periodicity in comparison to the flux of other energy ranger.