BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:André David Mendes (CERN) DTSTART:20200521T163000Z DTEND:20200521T173000Z DTSTAMP:20260423T003256Z UID:MPML/8 DESCRIPTION:Title: How we discovered the Higgs ahead of schedule - ML's role in unveiling the ke ystone of elementary particle physics\nby André David Mendes (CERN) a s part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nA bstract\nn 2010\, when the LHC started colliding proton pairs in earnest\, multi-variate analyses were newfangled methods starting to make inroads i n experimental particle physics. These methods faced widespread skepticism as to their performance and biases\, reflecting a winter of suspicion ove r overtrained neural networks that set in in the late 1990s. Thanks to mor e robust techniques\, like boosted decision trees\, it became possible to make better and more extensive use of the full information recorded in par ticle collisions at the Tevatron and LHC colliders.\n\nThe Higgs boson dis covery by the CMS and ATLAS collaborations in 2012 was only possible becau se of the use of multi-variate techniques that enhanced the sensitivity by up to the equivalent of having 50% more collision data available for anal ysis.\n\nWe will review the use of classification and regression in the Hi ggs to diphoton search and subsequent discovery\, a concrete example of a decade-old ML-based analysis in high-energy particle physics. Particular e mphasis will be placed in the modular design of the analysis and the inher ent explainability advantages\, used to great effect in assuaging concerns raised by hundreds of initially-skeptical colleagues in the CMS collabora tion.\nFinally\, we'll quickly highlight some particle physics challenges that have contributed to\, and made use of\, the last decade of graph\, ad versarial\, and deep ML developments.\n LOCATION:https://researchseminars.org/talk/MPML/8/ END:VEVENT END:VCALENDAR