LEADER 03507nam 22005895 450 001 9911049152103321 005 20260102120756.0 010 $a3-031-96288-5 024 7 $a10.1007/978-3-031-96288-2 035 $a(CKB)44769883400041 035 $a(MiAaPQ)EBC32471370 035 $a(Au-PeEL)EBL32471370 035 $a(DE-He213)978-3-031-96288-2 035 $a(EXLCZ)9944769883400041 100 $a20260102d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDevelopment of Machine Learning ? Trigger Algorithms and Search for Higgs Boson Pair Production $eIn the bb?? Decay Channel with the CMS Detector at the LHC /$fby Jona Motta 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (484 pages) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 311 08$a3-031-96287-7 327 $aHiggs boson pair production theoretical motivation -- The Compact Muon Solenoid at the Large Hadron Collider -- The Level-1 ?h trigger: from the past, to the present -- The Level-1 ?h trigger: from the present, to the future -- The search for HH ? bb? +? ? -- The results on HH ? bb? +? ? -- Conclusions. 330 $aThis book reports the successful optimization of the Compact Mupn Solenoid (CMS) tau trigger algorithm for the Run-3 (Phase-1) of the Large Hadron Collider (LHC) and a completely new and original design of a machine learning based tau triggering algorithm for the High Luminosity LHC (or Phase-2). A large proportion of searches at collider experiments relies on datasets collected with a dedicated tau lepton selection algorithm, particularly difficult to operate in intense hadronic environments, making the work descirbed in this book of prime importance. The second part of the book describes a major and very challenging data analysis, aiming to detect Higgs boson pair production. The book summarizes these contributions in clear, pedagogical prose while keeping an adequate and coherent balance between the technical and data analysis aspects. Machine learning techniques were used extensively throughout this research; therefore, special care has been taken to describe their core principles and application in high-energy physics, as well as potential future developments for sophisticated low-latency trigger algorithms and modern signal extraction methods. . 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 606 $aParticles (Nuclear physics) 606 $aQuantum field theory 606 $aMachine learning 606 $aParticles (Nuclear physics) 606 $aElementary Particles, Quantum Field Theory 606 $aMachine Learning 606 $aParticle Physics 615 0$aParticles (Nuclear physics) 615 0$aQuantum field theory. 615 0$aMachine learning. 615 0$aParticles (Nuclear physics) 615 14$aElementary Particles, Quantum Field Theory. 615 24$aMachine Learning. 615 24$aParticle Physics. 676 $a530.14 700 $aMotta$b Jona$01886863 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911049152103321 996 $aDevelopment of Machine Learning ? Trigger Algorithms and Search for Higgs Boson Pair Production$94522442 997 $aUNINA