{"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\/sv\/2023\/07\/groundbreaking-neural-network-supports-complex-physics-research\/","title":{"rendered":"Banbrytande neuralt n\u00e4tverk st\u00f6djer komplex fysikforskning"},"content":{"rendered":"

Inom den kr\u00e4vande experimentella fysiken hj\u00e4lper maskininl\u00e4rningstekniken (ML) forskare att analysera enorma men knapph\u00e4ndiga datam\u00e4ngder.\u00a0<\/b><\/p>\n

T\u00e4nk dig uppgiften att extrahera information fr\u00e5n en bok med tusen sidor, d\u00e4r varje sida bara inneh\u00e5ller en rad text. Att skanna en s\u00e5dan bok med en skanner som metodiskt l\u00e4ser varje sida tum f\u00f6r tum skulle vara oerh\u00f6rt m\u00f6dosamt och det mesta av arbetet skulle g\u00e5 \u00e5t till att skanna tomma ytor.\u00a0<\/span><\/p>\n

Liknande g\u00e4ller f\u00f6r experimentella fysiker, s\u00e4rskilt inom partikelfysiken. H\u00e4r registrerar och analyserar detektorer en \u00f6verv\u00e4ldigande m\u00e4ngd data, trots att bara en liten del av dem \u00e4r anv\u00e4ndbara.\u00a0<\/span><\/p>\n

Kazuhiro Terao, fysiker vid SLAC National Accelerator Laboratory, beskriver det s\u00e5 h\u00e4r: \"P\u00e5 ett fotografi av till exempel en f\u00e5gel som flyger p\u00e5 himlen kan varje pixel vara meningsfull\", men p\u00e5 de bilder som fysiker ofta granskar \u00e4r det bara en liten del som verkligen betyder n\u00e5got. Detta arbetsintensiva arbete sl\u00f6sar bort b\u00e5de tid och ber\u00e4kningsresurser.<\/span><\/p>\n

ML erbjuder en l\u00f6sning i form av SCNN (sparse convolutional neural networks). <\/span><\/p>\n

Genom att g\u00f6ra det m\u00f6jligt f\u00f6r forskare att zooma in p\u00e5 de viktiga delarna av sina data och filtrera bort resten, p\u00e5skyndar SCNN dataanalys i realtid.\u00a0<\/span><\/p>\n

\u00c5r 2012 f\u00f6rs\u00f6kte Benjamin Graham, som d\u00e5 var verksam vid University of Warwick, skapa ett neuralt n\u00e4tverk f\u00f6r att k\u00e4nna igen kinesisk handstil. Han justerade convolutional neural networks (CNN), en referensform av neurala n\u00e4tverk, f\u00f6r att g\u00f6ra dem mer l\u00e4mpade f\u00f6r data som anses vara \"glesa\", som en bild av ett kinesiskt tecken.\u00a0<\/span><\/p>\n

Graham erbjuder en annan analogi f\u00f6r glesa data: t\u00e4nk dig att Eiffeltornet var inneslutet i den minsta m\u00f6jliga rektangeln, den rektangeln skulle vara \"99,98 procent luft och bara 0,02 procent j\u00e4rn\". <\/span><\/p>\n

Om du bara bryr dig om metallen analyserar du en enorm m\u00e4ngd v\u00e4rdel\u00f6st utrymme.\u00a0<\/span><\/p>\n

Tillkomsten av SCNN<\/span><\/h2>\n

Efter att ha lyckats skapa ett system som effektivt kunde k\u00e4nna igen handskriven kinesiska gick Graham vidare till en st\u00f6rre utmaning: att k\u00e4nna igen 3D-objekt.<\/span><\/p>\n

Han f\u00f6rfinade sin teknik och publicerade detaljerna i den f\u00f6rsta <\/span>SCNN under 2017<\/span><\/a> p\u00e5 Facebook AI Research.<\/span><\/p>\n

Terao introducerade detta <\/span>koncept till partikelfysik<\/span><\/a> vid Fermi National Accelerator Laboratory, som unders\u00f6ker neutriner (de mest sv\u00e5rf\u00e5ngade och rikligt f\u00f6rekommande partiklarna med massa i universum). <\/span><\/p>\n

Terao ins\u00e5g att SCNN kunde optimera dataanalysen f\u00f6r Deep Underground Neutrino Experiment (DUNE), d\u00e4r han noterade att SCNN bearbetade data snabbare och mer effektivt \u00e4n traditionella metoder.\u00a0<\/span><\/p>\n

Ut\u00f6ver neutrinoforskningen planerar fysikern Philip Harris vid Massachusetts Institute of Technology att anv\u00e4nda SCNN vid Large Hadron Collider (LHC) vid CERN.\u00a0<\/span><\/p>\n

Harris hoppas att SCNN kan p\u00e5skynda dataanalysen vid LHC med minst en faktor 50 och bidra till att uppt\u00e4cka partiklar av s\u00e4rskilt intresse.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Inom den kr\u00e4vande experimentella fysiken hj\u00e4lper maskininl\u00e4rningstekniken (ML) forskarna att analysera enorma men knapph\u00e4ndiga datam\u00e4ngder. T\u00e4nk dig uppgiften att extrahera information fr\u00e5n en bok med tusen sidor som var och en bara inneh\u00e5ller en rad text. Att skanna en s\u00e5dan bok med en skanner som metodiskt l\u00e4ser varje sida tum f\u00f6r tum skulle vara oerh\u00f6rt m\u00f6dosamt, och det mesta av arbetet skulle g\u00e5 \u00e5t till att skanna tomma ytor. Liknande g\u00e4ller f\u00f6r experimentella fysiker, s\u00e4rskilt inom partikelfysiken. H\u00e4r registrerar och analyserar detektorer en \u00f6verv\u00e4ldigande m\u00e4ngd data, trots att bara en liten del av dem \u00e4r anv\u00e4ndbara. Som Kazuhiro Terao, fysiker vid<\/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":"\nGroundbreaking 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 rel=\"canonical\" href=\"https:\/\/dailyai.com\/sv\/2023\/07\/groundbreaking-neural-network-supports-complex-physics-research\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Groundbreaking neural network supports complex physics research | DailyAI\" \/>\n<meta property=\"og: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 property=\"og:url\" content=\"https:\/\/dailyai.com\/sv\/2023\/07\/groundbreaking-neural-network-supports-complex-physics-research\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-16T17:35:53+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-17T09:01:53+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/07\/shutterstock_1287557623.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta 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