INTRODUCTION TO PARTICLE ACCELERATORS AND DETECTORS
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- English course description
- Anno accademico
- 2022/2023
- Docente
- GIANLUIGI CIBINETTO
- Crediti formativi
- 6
- Periodo didattico
- Secondo Semestre
- SSD
- FIS/01
Obiettivi formativi
- In questa serie di lezione verranno fornite conoscenze di base della storia e delle applicazioni degli acceleratori di particelle e dei progressi nelle tecniche di rivelazione per fisica nucleare, delle particelle, fisica medica e dei materiali.
Prerequisiti
- Meccanica classica, elettromagnetismo, cinematica relativistica e conoscenza di base di interazione radiazione-materia.
Contenuti del corso
- List of topics
I) particle accelerator issue
Introduction to the course: objectives, pre-requisites, syllabus, methodology, evaluation. Overview of the field. Review of classical mechanics, relativistic kinematics and electromagnetism.
Applications of accelerators. Nuclear and particle physics: nuclear reactions, structure of matter, new particles and new physics. Synchrotron light sources and spallation neutron sources for biology and material science. Medical diagnostics and therapy: isotope production, cancer therapy. Art, archaeology, environment: carbon
dating, elemental analysis. Industrial applications: ion implantation, precision machining, sterilization. Energy conversion: accelerator-driven systems.
Evolution of particle accelerators: electrostatic machines, cyclotrons, linacs, betatrons, synchrotrons, colliders. Weak and strong focusing. Phase stability. Synchrotron radiation. Examples of existing complexes.
Luminosity. Fixed target and collider configurations. Crossing angles. Time structure. Instantaneous vs. average vs. integrated luminosity. Invariant formulation of event rate. Typical cross sections. Experiment data taking time.
Separation of transverse and longitudinal dynamics. Longitudinal dynamics. Phase stability. Motion in phase-energy plane. Transition energy. Phase-slip factor. Synchrotron frequency. Buckets.
Accelerators as dynamical systems. Continuous and discrete descriptions. Phase-space portraits. Stable and unstable fixed points. Flows. Dissipative systems. Nonlinear and chaotic dynamics.
Transverse linear dynamics. Coupled and uncoupled systems. Coordinates. Normalized magnetic gradients. Transfer matrices. Beam transport. Stability conditions. Equations of transverse motion. Hill's equation. Courant-Snyder parameterization: amplitude (beta) functions, betatron tune. Emittance.
Dispersion. Chromaticity. Lattice imperfections. Resonances. Tune diagram. Nonlinearities in accelerators: magnet imperfections, self fields, beam-beam forces. Tune spread generation. Dynamic aperture.
Discussion of course evaluation and student final report. Seminar on a current experimental research topic (nonlinear integrable optics, quantum radiation from single confined electrons, ...). Resources for students: textbooks, accelerator schools, conferences, journals. Research opportunities.
II) particle detector issue
Introduction to the course: objectives, pre-requisites. Overview of the field. Review particle interactions.
Gas detectors: diffusions in gases; magnetic field effect; cluster counting; MPGD; detectors for high rate, high radiation; timing with gas detectors.
Silicon detectors: theory of silicon detectors; strip detectors; pixel detectors, MAPS; applications.
Taking system: momentum resolution, multiple scattering, examples of tracking systems and performance.
Calorimetry: photon detection; electromagnetic claorimeters; hadronic calorimeters; particle flow calorimeters;
Particle identification applications (Cherenkov, RICH, TOF, muon identifications, …)
Neutron detection.
Electronics, trigger and DAQ systems.
Detector applications to HEP experiments, satellite experiments, medical physics, environmental monitoring.
Simulation techniques. Elements of statistics for particle detectors; parametric and full simulations. simulations tools: Garfield, Fluka, Ansys, GEANT4. Hands-on session.
Elements of reconstruction algorithms: clustering, pattern recognitions, track finding, track fitting, kalman filter, neural networks. Metodi didattici
- Lezioni frontali ed esercizi.
Modalità di verifica dell'apprendimento
- Le conoscenze teoriche e le abilita' acquisite durante verranno valutati attraverso elaborati scritti e esami orali
Testi di riferimento
- Appunti del corso e bibliografia selezionata.
