Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
10 credits
52.5 h + 7.5 h
Q2
Teacher(s)
Bruno Giacomo; Cortina Gil Eduardo; Delaere Christophe; Vischia Pietro (compensates Bruno Giacomo);
Language
English
Main themes
Advanced particle detectors ' Particle physics experiment design ' Triggering, data acquisition and computing systems ' Data reconstruction algorithms ' Advanced statistics ' Software tools for simulation in particle physics.
Aims
At the end of this learning unit, the student is able to : | |
1 |
a. Contribution of the teaching unit to the learning outcomes of the programme (PHYS2M and PHYS2M1) 1.3, 1.4, 1.5, 1.6, 2.2, 2.3, 2.4, 2.5, 5.1, 5.3, 6.1, 6.2, 6.3, 6.4, 7.1, 7.3 , 8.1, 8.2. b. Specific learning outcomes of the teaching unit At the end of this teaching unit, the student will be able to :
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Content
1. Signal formation : general case.
2. Tracking detectors.
a. Large area counters: hodoscopes.
b. Magnetic spectrometers : magnets, resolution.
c. Gas position detectors : MWPC, drift detectors, jet chambers, TPCs, RPCs.
d. Solid state position detectors : silicon detectors, scintillation fiber detectors.
e. LAr TPCs. Double phase TPCs.
3. Calorimetry.
a. Electromagnetic calorimeters.
b. Hadronic calorimeters.
c. Low temperature calorimeters. Bolometers.
4. Particle identification.
a. Muon detectors.
b. Cerenkov detectors : threshold, differential, RICH.
c. TRD detectors.
d. Time of flight.
e. dE/dx.
5. Complex detector study : journal club like approach.
a. Collider : CMS, DELPHI.
b. Fixed target : NA62.
c. Astroparticle : AMS-02, Auger.
6. Auxiliary systems.
a. Low and high voltage systems.
b. Gas systems.
c. Cooling systems.
d. Mechanical supports.
e. Cabling.
7. Nuclear electronics.
8. Trigger and data acquisition systems.
9. Offline data processing systems.
10. Event reconstruction algorithms.
a. Tracking,
b. Vertexing.
c. Clustering.
d. Jets
11. Calibration and alignment techniques.
12. Statistical methods of data analysis.
13. Simulation of particle propagation in matter.
14. Projects concerning either the simulation of particle propagation in matter or real particle detection systems in the laboratory or a statistical analysis of data from a physics experiment.
2. Tracking detectors.
a. Large area counters: hodoscopes.
b. Magnetic spectrometers : magnets, resolution.
c. Gas position detectors : MWPC, drift detectors, jet chambers, TPCs, RPCs.
d. Solid state position detectors : silicon detectors, scintillation fiber detectors.
e. LAr TPCs. Double phase TPCs.
3. Calorimetry.
a. Electromagnetic calorimeters.
b. Hadronic calorimeters.
c. Low temperature calorimeters. Bolometers.
4. Particle identification.
a. Muon detectors.
b. Cerenkov detectors : threshold, differential, RICH.
c. TRD detectors.
d. Time of flight.
e. dE/dx.
5. Complex detector study : journal club like approach.
a. Collider : CMS, DELPHI.
b. Fixed target : NA62.
c. Astroparticle : AMS-02, Auger.
6. Auxiliary systems.
a. Low and high voltage systems.
b. Gas systems.
c. Cooling systems.
d. Mechanical supports.
e. Cabling.
7. Nuclear electronics.
8. Trigger and data acquisition systems.
9. Offline data processing systems.
10. Event reconstruction algorithms.
a. Tracking,
b. Vertexing.
c. Clustering.
d. Jets
11. Calibration and alignment techniques.
12. Statistical methods of data analysis.
13. Simulation of particle propagation in matter.
14. Projects concerning either the simulation of particle propagation in matter or real particle detection systems in the laboratory or a statistical analysis of data from a physics experiment.
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
1. Theory classes and exercises.- Lectures in auditorium.
- Resolution of problems .
2. Laboratory sessions (7.5h). Mandatory presence at the following laboratories :
- Large-area cosmic ray detector ;
- Silicon sensors characterization ;
- Construction of an RPC detector.
3. Personal software project and report writing
Evaluation methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
Evaluation of reports written by the students on projects concerning either the simulation of the particle propagation in matter or real systems for particle detection in a laboratory or a statistical analysis of data resulting from an experiment in physics. Evaluation of an oral interrogation on the projects and the subjects treated in the teaching unit.
Bibliography
C. Grupen, B. Schwartz, “Particle Detectors” (2nd edition).
D. Green, “The Physics of Particle Detectors”.
R. Fernow, “Introduction to Experimental Particle Physics”.
C. Leroy, P.G. Rancoita, “Principles of Radiation Interaction in Matter and Detection”.
S. Tavernier, “Experimental Techniques in Nuclear and Particle Physics”.
G. Cowan, “Statistical Data Analysis”, Oxford Science Publications.
D. Green, “The Physics of Particle Detectors”.
R. Fernow, “Introduction to Experimental Particle Physics”.
C. Leroy, P.G. Rancoita, “Principles of Radiation Interaction in Matter and Detection”.
S. Tavernier, “Experimental Techniques in Nuclear and Particle Physics”.
G. Cowan, “Statistical Data Analysis”, Oxford Science Publications.
Teaching materials
- Syllabus, transparents du cours, bulletins d’exercices, examens des sessions précédentes, cahiers de laboratoire et tutoriels pour le programme de simulation sont disponibles sur le site MoodleUCL de l’unité d’enseignement.
Faculty or entity
PHYS