| Description |
1 online resource |
| Series |
Springer theses, 2190-5061 |
|
Springer theses, 2190-5061
|
| Contents |
Introduction -- Particle Physics at the Large Hadron Collider -- Shower Physics and Calorimetry -- CMS Calorimeter Endcap Upgrade (HGCAL) -- Strategy -- Experimental Infrastructure -- Data Reconstruction Algorithms -- Silicon Sensor and Module Qualification -- In Situ Calibration of Prototype Modules -- Performance Validation of the Silicon-Based Calorimeter Prototype -- Fast Generative Modelling of Electromagnetic Calorimeter Showers -- Summary, Outlook and Conclusion |
| Summary |
The Standard Model of Particle of Physics (SM), despite its success, still fails to provide explanations for some essential questions such as the nature of dark matter or the overabundance of matter over anti-matter in the universe. Therefore, experimental testing of this theory will remain a cornerstone of particle physics in the upcoming decades. A central approach is via collisions of elementary particles at the highest-possible centre-of-mass energies and rates. At the Large Hadron Collider (LHC), protons are accelerated to up to 7 TeV and are brought to collision 40 million times a second. Characterisation of the particles emerging from these collisions allow one to infer the underlying physical interactions. The particle energies are measured with calorimeters, themselves an integral component of the scientific programme of the LHC and prerequisite for its success. Facing increased radiation levels and more challenging experimental conditions after the upcoming High Luminosity upgrade of the Large Hadron Collider, the CMS collaboration will soon replace its current calorimeter endcaps with the High Granularity Calorimeter (HGCAL) in the mid 2020s. This thesis documents two milestones towards the realization of this novel and ambitious calorimeter concept: Prototypes of the silicon-based compartment have been built, operated in particle beam and ultimately its design could be validated. Furthermore, the thesis demonstrates the applicability of a specific set of deep learning algorithms for the generative modelling of granular calorimeter data. Besides the main results themselves, the thesis discusses in detail the associated experimental infrastructure and the underlying data reconstruction strategy and algorithms. It also incorporates short introductions to particle physics at the LHC, to calorimeter concepts and to the CMS HGCAL upgrade |
| Notes |
"Doctoral thesis accepted by the Rheinisch Westfälische Technische Hochschule, Aachen, Germany." |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed February 9, 2022) |
| Subject |
Calorimeters.
|
|
calorimeters.
|
|
Calorimeters
|
| Form |
Electronic book
|
| ISBN |
9783030902025 |
|
3030902021 |
|
