[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120983-en":3,"doc-seo-120983-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},120983,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","A multi-purpose reconstruction method based on machine learning for atmospheric neutrinos at JUNO","The Jiangmen Underground Neutrino Observatory (JUNO) experiment aims to determine the neutrino mass ordering (NMO) using a 20-kton liquid scintillator detector, with reactor neutrino oscillation measurements providing primary sensitivity. Atmospheric neutrino oscillations add independent NMO sensitivity through matter effects and can strengthen joint analyses. This contribution introduces a machine-learning-based, multi-purpose reconstruction approach for few-GeV atmospheric neutrinos by extracting event-topology features from PMT waveforms. Simulation studies show strong performance for directionality reconstruction and neutrino flavor identification, with potential applicability to similar liquid scintillator detectors.","A multi-purpose reconstruction method based on machine learning for atmospheric neutrinos at JUNO  \nHongyue Duyang1 , ∗ , Teng Li1 , Jiaxi Liu2 , Zhen Liu2 ,, Wuming Luo2 ,, Wing Yan Ma1 , Xiaohan Tan 1 , Zekun Yang1 , and Fanrui Zeng1 on behalf of the JUNO collaboration  \n1 Shandong University, Qingdao 266237, People’s Republic of China  \n2Institute of High Energy Physics, Beijing 100049, People’s Republic of China  \nAbstract. The Jiangmen Underground Neutrino Observatory (JUNO) experi  \nment is designed to measure the neutrino mass ordering (NMO) using a 20-kton liquid scintillator (LS) detector. Besides the precise measurement of the reactor neutrino’s oscillation spectrum, an atmospheric neutrino oscillation measurement in JUNO offers independent sensitivity for NMO, which can potentially increase JUNO’s total sensitivity in a joint analysis. In this contribution, we present a novel multi-purpose reconstruction method for atmospheric neutrinos in JUNO at few-GeV based on a machine learning technique. This method extracts features related to event topology from PMT waveforms and uses them as inputs to machine learning models. A preliminary study based on the JUNO simulation shows good performances for event directionality reconstruction and neutrino flavor identification. This method also has a great application potential for similar LS detectors.  \n1 Introduction  \nThe Jiangmeng Underground Neutrino Observatory (JUNO) [1][2] is currently under construction in southern China. The main physics goal of JUNO is to determine the neutrino mass ordering (NMO) . JUNO’s central detector (CD, Figure 1) is a 20-kton large-volume liquid scintillator (LS) detector designed to precisely measure the reactor neutrino spectrum from the Taishan and Yangjiang nuclear power plants. The scintillation light produced by neutrino interactions in the JUNO CD is collected by 17612 20-inch photo-multiplier tubes (PMTs) and 25600 3-inch PMTs, providing a total PMT coverage of 78% .  \nWhile the JUNO NMO sensitivity is mainly from reactor neutrino oscillations in vacuum, atmospheric neutrino oscillations offer extra sensitivity to NMO via matter effects. A joint analysis of reactor and atmospheric neutrino oscillations can potentially maximize JUNO’s total sensitivity. Atmospheric neutrinos are produced by energetic cosmic rays interacting with the upper atmosphere. The atmospheric neutrino flux consists of νµ , ¯νµ , νe , and ¯νe , which can undergo charged current (CC) or neutral current (NC) interactions in the detector. The identification of neutrino flavor is critical for the measurement of oscillation probabilities and the extraction of the oscillation parameters. Besides, the directionality information is also mandatory since it determines the neutrino’s oscillation baseline length.  \n∗ e-mail: [duyang@sdu.edu.cn](duyang@sdu.edu.cn)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nFigure 1. Drawing of the JUNO CD design. The homogeneous LS detector is submerged in a water pool that serves as a Cherenkov detector to veto external backgrounds. On the top of the WP is the Top Tracker (TT) detector with plastic scintillators to further help in tagging cosmic ray muons.  \nHowever, LS detectors, while offering excellent energy resolution and low threshold and playing an important role in low-energy neutrino physics topics, are traditionally believed to have very limited capability for atmospheric neutrino measurements. This is because those homogeneous detectors do not provide direct tracking information and the Cherenkov light is too weak compared to the scintillation light to give directionality or particle identification information. No measurement of atmospheric neutrino oscillations in an LS detector has ever been reported before.  \nIn this proceedin","cbCaimy2tcw3pcn1","https://ap.wps.com/l/cbCaimy2tcw3pcn1","pdf",3295022,1,6,"English","en",105,"# Abstract\n# Introduction\n# Methodology","[{\"question\":\"What is the main physics objective of JUNO and how do atmospheric neutrinos contribute to it?\",\"answer\":\"JUNO is designed to determine the neutrino mass ordering (NMO). Atmospheric neutrino oscillations provide additional, independent NMO sensitivity via matter effects and can enhance combined analyses with reactor data.\"},{\"question\":\"How does the proposed method reconstruct atmospheric neutrino information in JUNO?\",\"answer\":\"The method extracts topology-related features from PMT waveforms in a limited readout window and trains machine learning models to map these features to event information such as directionality and flavor-related outputs.\"},{\"question\":\"What performance results are reported from the JUNO simulation study?\",\"answer\":\"Based on Monte Carlo simulation, the method shows good performances for reconstructing event directionality and for identifying neutrino flavor.\"}]","A multi-purpose reconstruction method based on machine learning for atmospheric neutrinos at JUNO | PDF",1785733173,15,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-multi-purpose-reconstruction-method-based-on-machine-learning-for-atmospheric-neutrinos-at-juno","",{"@graph":36,"@context":85},[37,54,68],{"@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/a-multi-purpose-reconstruction-method-based-on-machine-learning-for-atmospheric-neutrinos-at-juno/120983/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main physics objective of JUNO and how do atmospheric neutrinos contribute to it?","Question",{"text":75,"@type":76},"JUNO is designed to determine the neutrino mass ordering (NMO). Atmospheric neutrino oscillations provide additional, independent NMO sensitivity via matter effects and can enhance combined analyses with reactor data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method reconstruct atmospheric neutrino information in JUNO?",{"text":80,"@type":76},"The method extracts topology-related features from PMT waveforms in a limited readout window and trains machine learning models to map these features to event information such as directionality and flavor-related outputs.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results are reported from the JUNO simulation study?",{"text":84,"@type":76},"Based on Monte Carlo simulation, the method shows good performances for reconstructing event directionality and for identifying neutrino flavor.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]