{"id":2573,"date":"2023-07-16T17:35:53","date_gmt":"2023-07-16T17:35:53","guid":{"rendered":"https:\/\/dailyai.com\/?p=2573"},"modified":"2023-07-17T09:01:53","modified_gmt":"2023-07-17T09:01:53","slug":"groundbreaking-neural-network-supports-complex-physics-research","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2023\/07\/groundbreaking-neural-network-supports-complex-physics-research\/","title":{"rendered":"Banebrydende neuralt netv\u00e6rk underst\u00f8tter kompleks fysikforskning"},"content":{"rendered":"<p><b>Inden for den kr\u00e6vende eksperimentelle fysik hj\u00e6lper maskinl\u00e6ringsteknologi (ML) forskere med at analysere kolossale, men sparsomme datas\u00e6t.\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Forestil dig opgaven med at udtr\u00e6kke information fra en bog med tusind sider, der hver is\u00e6r kun indeholder \u00e9n linje tekst. At scanne s\u00e5dan en bog med en scanner, der metodisk l\u00e6ser hver side tomme for tomme, ville v\u00e6re enormt besv\u00e6rligt, og det meste af arbejdet ville v\u00e6re spildt p\u00e5 at scanne tomrum.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Det samme g\u00e6lder for eksperimentalfysikere, is\u00e6r inden for partikelfysik. Her registrerer og analyserer detektorerne en overv\u00e6ldende m\u00e6ngde data, selv om kun en lille procentdel er brugbar.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Som Kazuhiro Terao, der er fysiker ved SLAC National Accelerator Laboratory, beskriver det: \"P\u00e5 et fotografi af f.eks. en fugl, der flyver p\u00e5 himlen, kan hver eneste pixel v\u00e6re meningsfuld,\" men p\u00e5 de billeder, som fysikere ofte unders\u00f8ger, er det kun en lille del, der virkelig betyder noget. Denne arbejdskr\u00e6vende \u00f8velse spilder b\u00e5de tid og computerressourcer.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">ML tilbyder en l\u00f8sning i form af sparse convolutional neural networks (SCNN). <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ved at g\u00f8re det muligt for forskere at zoome ind p\u00e5 de vigtige dele af deres data og filtrere resten fra, fremskynder SCNN'er dataanalyse i realtid.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">I 2012 fors\u00f8gte Benjamin Graham, som dengang arbejdede p\u00e5 University of Warwick, at skabe et neuralt netv\u00e6rk, som kunne genkende kinesisk h\u00e5ndskrift. Han justerede convolutional neural networks (CNN'er), en referenceform for neurale netv\u00e6rk, for at g\u00f8re dem mere egnede til data, der betragtes som 'sparsomme', som et billede af et kinesisk tegn.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Graham tilbyder en anden analogi for sparsomme data: Forestil dig, at Eiffelt\u00e5rnet var indkapslet i det mindst mulige rektangel, det rektangel ville v\u00e6re \"99,98 procent luft og kun 0,02 procent jern\". <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hvis du kun er interesseret i metallet, analyserer du en enorm m\u00e6ngde ubrugelig plads.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Fremkomsten af SCNN'er<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Efter at have skabt et system, der effektivt kunne genkende h\u00e5ndskrevet kinesisk, gik Graham videre til en st\u00f8rre udfordring: at genkende 3D-objekter.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> Han forfinede sin teknik og offentliggjorde detaljerne i den f\u00f8rste <\/span><a href=\"https:\/\/ai.meta.com\/research\/publications\/mask-r-cnn\/\"><span style=\"font-weight: 400;\">SCNN i 2017<\/span><\/a><span style=\"font-weight: 400;\"> mens han var hos Facebook AI Research.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Terao introducerede dette <\/span><a href=\"https:\/\/www.quantamagazine.org\/sparse-neural-networks-point-physicists-to-useful-data-20230608\/\"><span style=\"font-weight: 400;\">koncept til partikelfysik<\/span><\/a><span style=\"font-weight: 400;\"> p\u00e5 Fermi National Accelerator Laboratory, som unders\u00f8ger neutrinoer (de mest flygtige og rigelige partikler med masse i universet). <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Terao erkendte, at SCNN'er kunne optimere dataanalysen for Deep Underground Neutrino Experiment (DUNE), hvor han bem\u00e6rkede, at SCNN'erne behandlede dataene hurtigere og mere effektivt end traditionelle metoder.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ud over neutrinoforskningen planl\u00e6gger fysikeren Philip Harris fra Massachusetts Institute of Technology at anvende SCNN'er ved Large Hadron Collider (LHC) p\u00e5 CERN.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Harris h\u00e5ber, at SCNN'er kan fremskynde dataanalysen ved LHC med mindst en faktor 50 og bidrage til at opdage partikler af s\u00e6rlig interesse.<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Inden for den kr\u00e6vende eksperimentelle fysik hj\u00e6lper maskinl\u00e6ringsteknologi (ML) forskere med at analysere kolossale, men sparsomme datas\u00e6t.  Forestil dig opgaven med at udtr\u00e6kke information fra en bog med tusind sider, der hver is\u00e6r kun indeholder \u00e9n linje tekst. At scanne s\u00e5dan en bog med en scanner, der metodisk l\u00e6ser hver side tomme for tomme, ville v\u00e6re enormt besv\u00e6rligt, og det meste af indsatsen ville v\u00e6re spildt p\u00e5 at scanne tomrum.  Det samme g\u00e6lder for eksperimentalfysikere, is\u00e6r inden for partikelfysik. Her registrerer og analyserer detektorer en overv\u00e6ldende m\u00e6ngde data, selv om kun en lille procentdel er brugbar.  Som Kazuhiro Terao, der er fysiker ved<\/p>","protected":false},"author":2,"featured_media":2574,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[127,221,105,222,135],"class_list":["post-2573","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai","tag-cern","tag-machine-learning","tag-ml","tag-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Groundbreaking neural network supports complex physics research | DailyAI<\/title>\n<meta name=\"description\" content=\"In the demanding realm of experimental physics, machine learning (ML) technology is helping researchers analyze colossal but sparse data sets.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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