Muutke küpsiste eelistusi

Development of Machine Learning Trigger Algorithms and Search for Higgs Boson Pair Production: In the bb Decay Channel with the CMS Detector at the LHC [Kõva köide]

  • Formaat: Hardback, 296 pages, kõrgus x laius: 235x155 mm, 152 Illustrations, color; 1 Illustrations, black and white; XVIII, 296 p. 153 illus., 152 illus. in color., 1 Hardback
  • Sari: Springer Theses
  • Ilmumisaeg: 09-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031962877
  • ISBN-13: 9783031962875
Teised raamatud teemal:
  • Kõva köide
  • Hind: 150,61 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 177,19 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Hardback, 296 pages, kõrgus x laius: 235x155 mm, 152 Illustrations, color; 1 Illustrations, black and white; XVIII, 296 p. 153 illus., 152 illus. in color., 1 Hardback
  • Sari: Springer Theses
  • Ilmumisaeg: 09-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031962877
  • ISBN-13: 9783031962875
Teised raamatud teemal:
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, 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. 
 
Higgs 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.
Jona Motta is a particle physicist from Italy, born in 1996.   He obtained his B.Sc. degree in Physics at the University of Milano Bicocca, with a dissertation entitled "Performance studies for Higgs pair searches at LHC with the CMS detector" under the supervision of Dr. Pietro Govoni.   He obtained a Joint M.Sc. degree in High Energy Physics at ETH Zürich and École Polytechnique Paris, with two dissertations titled "Testing Lepton Flavour Universality in semi-leptonic decays of the Bc+ meson: a feasibility study in CMS" under the supervision of Prof. Dr. Günther Dissertori, and "Study of the Higgs boson self-coupling in the bb decay channel" under the supervision of Dr. Roberto Salerno. During his studies, Jona joined the CMS Collaboration in 2020.   Jona worked on his Ph.D. thesis at the Laboratoire Leprince Ringuet (LLR) at the École Polytechnique in Paris, working on the development of a completely new and original design of a machine learning based triggering algorithm for CMS at the High Luminosity LHC (or Phase-2), and searching for Higgs boson pair production in the bb final state.   He is currently a postdoctoral researcher at the University of Zürich, and his main research interests are the search for Higgs boson pair production and the searches for additional bosons that could reveal the presence of physics beyond the Standard Model. Alongside these physics interests, Jona continues to develop machine learning techniques that aim at boosting the sensitivy of physics analyses at CMS.