Development of Machine Learning t Trigger Algorithms and Search for Higgs Boson Pair Production

In the bbtt Decay Channel with the CMS Detector at the LHC

Éditeur :

Springer

Paru le : 2026-01-01

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 ...
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Auteur

Éditeur

Collection
n.c

Parution
2026-01-01

Pages
350 pages

EAN papier
9783031962875

Auteur(s) du livre


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 bbττ 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 τ triggering algorithm for CMS at the High Luminosity LHC (or Phase-2), and searching for Higgs boson pair production in the bbττ 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.

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EAN PDF
9783031962882
Prix
158,24 €
Nombre pages copiables
3
Nombre pages imprimables
35
Taille du fichier
45650 Ko
EAN EPUB
9783031962882
Prix
158,24 €
Nombre pages copiables
3
Nombre pages imprimables
35
Taille du fichier
91556 Ko

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