[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125720-en":3,"doc-seo-125720-105":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},125720,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Detecting Fast Neutrino Flavor Conversions with Machine Learning - slideshare","Fast flavor conversions of neutrinos can emerge in dense astrophysical environments such as core-collapse supernovae and neutron star mergers when the angular distribution of neutrino lepton number crosses zero. This study improves prior machine-learning approaches by testing robustness with realistic CCSN simulation data using full Boltzmann neutrino transport, analyzing bias-variance behavior with artificial datasets, and proposing ways to enhance them. The methods are extended to heavy-leptonic channels to handle cases where νx and ν¯x differ, enabling evaluation of FFC occurrence across CCSN and NSM models.","MPP-2023-237  \nDetecting Fast Neutrino Flavor Conversions with Machine Learning  \narXiv :2310 .03807v2 [ astro-ph .HE] 5 Feb 2024  \nSajad Abbar 1 and Hiroki Nagakura 2  \n1 Max-Planck-Institut f¨ur Physik (Werner-Heisenberg-Institut), F¨ohringer Ring 6, 80805 M¨unchen, Germany  \n2 Division of Science, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  \nNeutrinos in dense environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo fast flavor conversions (FFCs) once the angular distribution of neutrino lepton number crosses zero along a certain direction. Recent advancements have demonstrated the effectiveness of machine learning (ML) in detecting these crossings. In this study, we enhance prior research in two significant ways. Firstly, we utilize realistic data from CCSN simulations, where neutrino transport is solved using the full Boltzmann equation. We evaluate the ML methods’adaptability in a real-world context, enhancing their robustness. In particular, we demonstrate that when working with artificial data, simpler models outperform their more complex counterparts, a noteworthy illustration of the bias-variance tradeoff in the context of ML. We also explore methods to improve artificial datasets for ML training. In addition, we extend our ML techniques to detect the crossings in the heavy-leptonic channels, accommodating scenarios where νx and ν¯x may differ. Our research highlights the extensive versatility and effectiveness of ML techniques, presenting an unparalleled opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations.  \nI. INTRODUCTION  \nCore-collapse supernovae (CCSNe) and neutron star mergers (NSMs) are cataclysmic stellar events that represent the dramatic culmination of massive stars’ life cyclesand the collision and coalescence of incredibly dense remnants, respectively [1–4] . These events not only mark the end of massive stars and dense objects, but also unveil some of the most energetic and enigmatic phenomena in the universe. In the heart of these cosmic fireworks, oneof the most fascinating processes at play is the neutrino emission, which are released in vast quantities during CCSNe and NSMs.  \nAs they journey through the extraordinarily dense and extreme conditions within these events, neutrinos undergo an intriguing phenomenon known as collective neutrino oscillations. This fascinating behavior arises from their interactions with the dense background neutrino gas, where coherent forward scatterings play a pivotal role. This phenomenon occurs in a nonlinear and collective manner, creating a rich tapestry of flavor transformations [5–11] (for a recent review see Ref. [12]) .  \nOf particular interest are the so-called fast flavor conversions (FFCs), which occur on scales characterized by ∼ G−F1 n1 (see, e.g., Refs. [13–60]) . Here, GF represents the Fermi coupling constant, and nν denotes the neutrino number density. These FFCs can take place on timescales much shorter than what would be expected in the vacuum.  \nFFCs occur iff the angular distribution of the neutrino lepton number, defined as,  \nG (v) = √2GF Z0 ∞ E(2~~ν~~2dπE)3ν [ 􀀀fνe (p) − fνx (p)􀀁 (1)  \n−􀀀fν¯e (p) − fν¯x (p)􀀁],  \ncrosses zero at some v = v (µ,ϕν ), with µ = cosθν [30] . Here, Eν , θν , and ϕν are the neutrino energy, the zenith, and azimuthal angles of the neutrino velocity, respec-  \ntively, and fν ’s are the neutrino occupation numbers. When νx and ν¯x have similar angular distributions, a scenario commonly observed in state-of-the-art CCSN simulations, this expression transforms into the conventional νELN (neutrino electron lepton number) .  \nExploring νELN crossings necessitates access to the complete angular distributions of neutrinos. However, obtaining such detailed angular information poses a significant challenge in most cutting-edge CCSN and NSM simulations due to the prohibitive computational demands involved.  \nAs a practical alter","cbCaipAvmdPn29xe","https://ap.wps.com/l/cbCaipAvmdPn29xe","pdf",612063,1,12,"English","en",105,"# Introduction\n## Neutrino collective oscillations and fast flavor conversions\n## Angular crossings and ELN/ELN moment simplifications\n## Motivation for machine learning detection","[{\"question\":\"What physical condition triggers fast neutrino flavor conversions?\",\"answer\":\"Fast flavor conversions occur when the angular distribution of neutrino lepton number crosses zero along a certain direction.\"},{\"question\":\"Why is full angular information difficult to obtain in CCSN/NSM simulations?\",\"answer\":\"Because resolving complete angular distributions is computationally prohibitive in most cutting-edge simulations, the study instead uses manageable angular moments.\"},{\"question\":\"How does the paper extend machine learning beyond the basic detection setup?\",\"answer\":\"It evaluates ML robustness using realistic CCSN data, examines bias-variance effects with artificial datasets, improves artificial training data, and extends detection to heavy-leptonic channels to accommodate different νx and ν¯x angular distributions.\"}]","Detecting Fast Neutrino Flavor Conversions with Machine Learning - slideshare | PDF",1785900839,30,{"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},"detecting-fast-neutrino-flavor-conversions-with-machine-learning-slideshare","",{"@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/detecting-fast-neutrino-flavor-conversions-with-machine-learning-slideshare/125720/",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","2026-08-05",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 physical condition triggers fast neutrino flavor conversions?","Question",{"text":75,"@type":76},"Fast flavor conversions occur when the angular distribution of neutrino lepton number crosses zero along a certain direction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is full angular information difficult to obtain in CCSN/NSM simulations?",{"text":80,"@type":76},"Because resolving complete angular distributions is computationally prohibitive in most cutting-edge simulations, the study instead uses manageable angular moments.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper extend machine learning beyond the basic detection setup?",{"text":84,"@type":76},"It evaluates ML robustness using realistic CCSN data, examines bias-variance effects with artificial datasets, improves artificial training data, and extends detection to heavy-leptonic channels to accommodate different νx and ν¯x angular distributions.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"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"]