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
Erscheinungsjahr 2026
ISBN: 978-3-031-96287-5
Verlag: Springer

Buch, Englisch, 350 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 795 g

Reihe: Springer Theses

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.

Zielgruppe

Research

Autoren/Hrsg.

Motta, Jona

Fachgebiete

• Naturwissenschaften Physik Quantenphysik Teilchenphysik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Higgs boson pair production theoretical motivation.- The Compact Muon Solenoid at the Large Hadron Collider.- The Level-1 th trigger: from the past, to the present.- The Level-1 th trigger: from the present, to the future.- The search for HH ? bbt +t -.- The results on HH ? bbt +t -.- Conclusions.

Über Autor(innen)

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