[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120949-en":3,"doc-seo-120949-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120949,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Earth’s radiation belts - From machine learning to physical understanding","This dissertation investigates how machine learning can be used to study Earth’s radiation belts and to extract physically grounded conclusions from data. Using high-quality Van Allen Probes observations, it develops neural-network models (ORIENT) for electron fluxes from 50 keV to several MeV with inputs limited to a few days of solar-wind history and geomagnetic indices. The models reproduce key storm-time electron dynamics and, via DeepSHAP feature attribution, identify meaningful drivers of enhancement and depletion responses.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nEarth’s radiation belts: From machine learning to physical understanding  \nPermalink  \n[https://escholarship.org/uc/item/1b65t5s5](https://escholarship.org/uc/item/1b65t5s5)  \nAuthor  \nMa, Donglai  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nEarth's radiation belts:  \nFrom machine learning to physical understanding  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Atmospheric and Oceanic Sciences  \nby  \nDonglai Ma  \n2023  \n© Copyright by Donglai Ma 2023  \nABSTRACT OF THE DISSERTATION  \nEarth's radiation belts:  \nFrom machine learning to physical understanding  \nby  \nDonglai Ma  \nDoctor of Philosophy in Atmospheric and Oceanic Sciences University of California, Los Angeles, 2023  \nProfessor Jacob Bortnik, Chair  \nThis dissertation explores how machine learning can be used to study the Earth's radiation belts and the physical conclusions we derive from machine learning. To be concrete, the Earth's radiation belts contain many high-energy electrons, with their energies ranging from kilo-electron volts (keV) to several Mega-electron volts (MeV) . This radiation environment, exhibiting rich dynamical variations, is known to be particularly hazardous to spacecraft and is di􀀎cult to predict, particularly because of the delicate balance between acceleration, transport, and loss, combined with the many di􀀋erent physical processes that produce thesee􀀋ects. With high-quality data from the Van Allen Probes mission, we present a set of machine-learning-based models of electron 􀀍uxes ranging from 50 keV to several MeV using a neural network approach in the Earth's outer radiation belt. The Outer RadIation belt Electron Neural neT model (ORIENT) uses only a few days of the history of solar wind conditions and geomagnetic indices as input. The models show great performance (R2 􀀘 0:7 􀀀 0:9) on the out-of-sample dataset and are able to capture electron dynamics such as intensi􀀌cations, decays, dropouts, and the Magnetic Local Time dependence of the lower  \nenergy (􀀘 \u003C 100 keV) electron 􀀍uxes during storms. Motivated by the great performance of the machine learning model, we realize that the trained model contain the information of the repeated magnetospheric dynamics driven by solar activity. Thus, we utilize a stateof-the-art feature attribution method called DeepSHAP, which was based on Shapley values in game theory, to explain the behavior of the ORIENT model at a representative electron energy of 􀀘 1 MeV during a storm time event and a non-storm time event. The results show that the feature importance calculated from the purely data-driven ORIENT model identi􀀌es physically meaningful behaviors such as magnetopause shadowing, substorm-driven acceleration, and Dst e􀀋ect. We then combine this method with superposed epoch analysis to identify the long-debated question: What causes the radiation belt electrons to have two di􀀋erent responses, namely `enhancement' and `depletion' to storms? Our feature attribution results indicate that the depletion events can be thought of essentially as \\non-acceleration\"events that occur when substorm activity following the pressure maximum is not su􀀎cient to accelerate the 􀀍uxes above its pre-storm level. The results show that average AL over storm-time period and recovery phase has a signi􀀌cant correlation with the resulting 􀀍ux levels suggesting that it is important to incorporate the AL index history more directly into the radiation belt modeling. We then turn back to physics and build the statistical model of waves and density related to the AL index to create a Fokker-Planck simulation driven by time-varying geomagnetic activity. The result reproduces the enhancement of electrons at the ultra-relativistic range very well. The ","cbCail0FmUmxkdLp","https://ap.wps.com/l/cbCail0FmUmxkdLp","pdf",63481207,1,165,"English","en",105,"# 1 Introduction\n## 1.1 The structure of the Earth's magnetosphere and Space Weather\n## 1.2 Particle motion in the radiation belts\n## 1.3 Plasma waves in magnetosphere and wave-particle interaction\n## 1.4 Acceleration, loss, and transport of radiation belt electrons\n## 1.5 Outline and objectives of the thesis\n# 2 Theoretical and simulation background\n## 2.1 The quasi-linear diffusion process\n## 2.2 Machine learning method: neural network\n## 2.3 Shapley values in Game theory\n# 3 ORIENT: Outer RadIation belt Electron Flux Neura","[{\"question\":\"What is the main goal of the dissertation on Earth’s radiation belts?\",\"answer\":\"To use machine learning to model radiation-belt electron fluxes and to derive physical understanding from those models.\"},{\"question\":\"What neural-network model is introduced, and what inputs does it use?\",\"answer\":\"The ORIENT model predicts electron fluxes using a small set of days of solar-wind history and geomagnetic indices as inputs.\"},{\"question\":\"How does the dissertation explain storm-time electron enhancements versus depletions?\",\"answer\":\"Feature attribution results indicate depletion can correspond to insufficient acceleration after pressure maxima, while a statistical wave-and-density model reproduces ultra-relativistic enhancement and reveals thresholds tied to substorm activity.\"}]","Earth’s radiation belts - From machine learning to physical understanding | PDF",1785732999,416,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"earths-radiation-belts-from-machine-learning-to-physical-understanding","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/earths-radiation-belts-from-machine-learning-to-physical-understanding/120949/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the dissertation on Earth’s radiation belts?","Question",{"text":76,"@type":77},"To use machine learning to model radiation-belt electron fluxes and to derive physical understanding from those models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What neural-network model is introduced, and what inputs does it use?",{"text":81,"@type":77},"The ORIENT model predicts electron fluxes using a small set of days of solar-wind history and geomagnetic indices as inputs.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the dissertation explain storm-time electron enhancements versus depletions?",{"text":85,"@type":77},"Feature attribution results indicate depletion can correspond to insufficient acceleration after pressure maxima, while a statistical wave-and-density model reproduces ultra-relativistic enhancement and reveals thresholds tied to substorm activity.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]