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A machine-learning-based electron density (MLED) model in the inner magnetosphere
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作者 QingHua Zhou YunXiang Chen +5 位作者 FuLiang Xiao Sai Zhang Si Liu Chang Yang YiHua He ZhongLei Gao 《Earth and Planetary Physics》 EI CSCD 2022年第4期350-358,共9页
Plasma density is an important factor in determining wave-particle interactions in the magnetosphere.We develop a machine-learning-based electron density(MLED)model in the inner magnetosphere using electron density da... Plasma density is an important factor in determining wave-particle interactions in the magnetosphere.We develop a machine-learning-based electron density(MLED)model in the inner magnetosphere using electron density data from Van Allen Probes between September 25,2012 and August 30,2019.This MLED model is a physics-based nonlinear network that employs fundamental physical principles to describe variations of electron density.It predicts the plasmapause location under different geomagnetic conditions,and models separately the electron densities of the plasmasphere and of the trough.We train the model using gradient descent and backpropagation algorithms,which are widely used to deal effectively with nonlinear relationships among physical quantities in space plasma environments.The model gives explicit expressions with few parameters and describes the associations of electron density with geomagnetic activity,solar cycle,and seasonal effects.Under various geomagnetic conditions,the electron densities calculated by this model agree well with empirical observations and provide a good description of plasmapause movement.This MLED model,which can be easily incorporated into previously developed radiation belt models,promises to be very helpful in modeling and improving forecasting of radiation belt electron dynamics. 展开更多
关键词 background electron density inner magnetosphere machine learning Van Allen Probes observation
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Study of the material photon and electron background and the liquid argon detector veto efficiency of the CDEX-10 experiment
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作者 苏健 曾志 +67 位作者 马豪 岳骞 程建平 常建平 陈楠 陈宁 陈庆豪 陈云华 庄又澄 邓智 杜强 宫辉 郝喜庆 何庆驹 黄瀚雄 黄腾锐 江灏 康克军 李浩斌 李荐民 李金 李军 李霞 李新颖 李学潜 李玉兰 李元景 廖恒毅 林枫凯 林欣德 刘书魁 吕岚春 毛绍基 覃建强 任杰 任婧 阮锡超 申满斌 SINGH Lakhwinder SINGH Manoj Kumar SOMA Arun Kumar 唐昌建 曾昭雄 王继敏 王力 王青 王子敬 吴世勇 吴玉成 幸浩洋 徐音 薛涛 杨丽桃 杨松纬 易难 喻纯旭 于昊 余训臻 曾雄辉 张岚 张蕴华 赵明刚 赵伟 周祖英 朱敬军 朱维彬 朱雪洲 朱忠华 《Chinese Physics C》 SCIE CAS CSCD 2015年第3期30-37,共8页
The China Dark Matter Experiment (CDEX) is located at the China Jinping Underground Laboratory (CJPL) and aims to directly detect the weakly interacting massive particles (WIMP) flux with high sensitivity in the... The China Dark Matter Experiment (CDEX) is located at the China Jinping Underground Laboratory (CJPL) and aims to directly detect the weakly interacting massive particles (WIMP) flux with high sensitivity in the low mass region. Here we present a study of tile predicted photon and electron backgrounds including the background contribution of the structure materials of the germanium detector, the passive shielding materials, and the intrinsic radioactivity of the liquid argon that serves as an anti-Compton active shielding detector. A detailed geometry is modeled and the background contribution has been simulated based on the measured radioactivities of all possible components within tile GEANT4 program. Then the photon and electron background level in the energy region of interest (〈10-2events-kg1·day 1·keV-1 (cpkkd)) is predicted based on Monte Carlo simulations. The simulated result is consistent with the design goal of the CDEX-10 experiment, 0.1cpkkd, which shows that the active and passive shield design of CDEX-10 is effective and feasible. 展开更多
关键词 CDEX-IO material photon and electron background germanium detector liquid argon veto coincidentcut Monte Carlo simulation
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