{"id":127337,"date":"2021-09-07T00:24:54","date_gmt":"2021-09-07T07:24:54","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2021\/09\/hunting-anomalies-with-an-ai-trigger"},"modified":"2021-09-07T00:24:54","modified_gmt":"2021-09-07T07:24:54","slug":"hunting-anomalies-with-an-ai-trigger","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2021\/09\/hunting-anomalies-with-an-ai-trigger","title":{"rendered":"Hunting anomalies with an AI trigger"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/hunting-anomalies-with-an-ai-trigger3.jpg\"><\/a><\/p>\n<p><i>CERN Courier<\/i><\/p>\n<hr>\n<p>Jennifer Ngadiuba and Maurizio Pierini describe how \u2018unsupervised\u2019 machine learning could keep watch for signs of new physics at the LHC that have not yet been dreamt up by physicists.<\/p>\n<p>In the 1970s, the robust mathematical framework of the Standard Model \u2120 replaced data observation as the dominant starting point for scientific inquiry in particle physics. Decades-long physics programmes were put together based on its predictions. Physicists built complex and highly successful experiments at particle colliders, culminating in the discovery of the Higgs boson at the LHC in 2012.<\/p>\n<p>Along this journey, particle physicists adapted their methods to deal with ever growing data volumes and rates. To handle the large amount of data generated in collisions, they had to optimise real-time selection algorithms, or triggers. The field became an early adopter of artificial intelligence (AI) techniques, especially those falling under the umbrella of \u201csupervised\u201d machine learning. Verifying the SM\u2019s predictions or exposing its shortcomings became the main goal of particle physics. But with the SM now apparently complete, and supervised studies incrementally excluding favoured models of new physics, \u201cunsupervised\u201d learning has the potential to lead the field into the uncharted waters beyond the SM.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>CERN Courier Jennifer Ngadiuba and Maurizio Pierini describe how \u2018unsupervised\u2019 machine learning could keep watch for signs of new physics at the LHC that have not yet been dreamt up by physicists. In the 1970s, the robust mathematical framework of the Standard Model \u2120 replaced data observation as the dominant starting point for scientific inquiry [\u2026]<\/p>\n","protected":false},"author":513,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41,2229,48,6],"tags":[],"class_list":["post-127337","post","type-post","status-publish","format-standard","hentry","category-information-science","category-mathematics","category-particle-physics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/127337","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/513"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=127337"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/127337\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=127337"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=127337"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=127337"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}