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Record Nr. |
UNINA9911049152103321 |
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Autore |
Motta Jona |
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Titolo |
Development of Machine Learning τ Trigger Algorithms and Search for Higgs Boson Pair Production : In the bbττ Decay Channel with the CMS Detector at the LHC / / by Jona Motta |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (484 pages) |
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Collana |
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Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5061 |
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Disciplina |
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Soggetti |
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Particles (Nuclear physics) |
Quantum field theory |
Machine learning |
Elementary Particles, Quantum Field Theory |
Machine Learning |
Particle Physics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Higgs boson pair production theoretical motivation -- The Compact Muon Solenoid at the Large Hadron Collider -- The Level-1 τh trigger: from the past, to the present -- The Level-1 τh trigger: from the present, to the future -- The search for HH → bbτ +τ − -- The results on HH → bbτ +τ − -- Conclusions. |
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Sommario/riassunto |
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This 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, |
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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. . |
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