Machine Learning Based Long Lived Particle Reconstruction Algorithm For Run 2 And Upgrade Lhcb Trigger And A Flexible Software Platform For The Ut Detector Read Out Chip Emulation

Machine Learning Based Long-lived Particle Reconstruction Algorithm for Run 2 and Upgrade LHCb Trigger and a Flexible Software Platform for the UT Detector Read-out Chip Emulation

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

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.

Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors

This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topics are pattern recognition for track and vertex finding, solving the equations of motion by analytical or numerical methods, treatment of material effects such as multiple Coulomb scattering and energy loss, and the estimation of track and vertex parameters by statistical algorithms. The material covers both established methods and recent developments in these fields and illustrates them by outlining exemplary solutions developed by selected experiments. The clear presentation enables readers to easily implement the material in a high-level programming language. It also highlights software solutions that are in the public domain whenever possible. It is a valuable resource for PhD students and researchers working on online or offline reconstruction for their experiments.