This learning unit is not being organized during year 2024-2025.
Teacher(s)
Bruno Giacomo; Cortina Gil Eduardo; de Wasseige Gwenhaël; Janquart Justin (compensates Cortina Gil Eduardo); Lemaitre Vincent;
Language
English
Prerequisites
LPHYS2131
Main themes
Principles and applications of particle acceleration - Accelerator physics - Neutrino physics - Precision measurements at low energies
Content
This course consists of 4 partims, each worth 2.5 credits: "Neutrino Physics", "Astroparticle Physics", "Gravitational-wave Physics", and "Data Analysis methods in Astrophysics".
Students can either choose to take the entire course, totalling 10 credits, or any pair of partims to total 5 credits. The description of the content of each partim is below:
PARTIM "Neutrino Physics" (2.5 credits): physics of neutrino interaction with matters with emphasis on neutrino oscillations, neutrinos produced at accelerators and nuclear plants, and cosmic neutrinos.
PARTIM "Astroparticle Physics" (2.5 credits): theory and detection methods in astroparticle physics.
PARTIM "Gravitational-wave Physics" (2.5 credits): theory and detection methods in gravitational-wave physics.
PARTIM "Data analysis methods in Astrophysics" (2.5 credits): data analysis techniques used in observational astrophysics. They include both classical statistical data analysis and machine learning- based techniques.
Students can either choose to take the entire course, totalling 10 credits, or any pair of partims to total 5 credits. The description of the content of each partim is below:
PARTIM "Neutrino Physics" (2.5 credits): physics of neutrino interaction with matters with emphasis on neutrino oscillations, neutrinos produced at accelerators and nuclear plants, and cosmic neutrinos.
PARTIM "Astroparticle Physics" (2.5 credits): theory and detection methods in astroparticle physics.
PARTIM "Gravitational-wave Physics" (2.5 credits): theory and detection methods in gravitational-wave physics.
PARTIM "Data analysis methods in Astrophysics" (2.5 credits): data analysis techniques used in observational astrophysics. They include both classical statistical data analysis and machine learning- based techniques.
Teaching methods
Lectures in class.
Personal projects. Students can choose the subject among a list proposed by the teachers.
Reading portfolio for personal study.
Personal projects. Students can choose the subject among a list proposed by the teachers.
Reading portfolio for personal study.
Evaluation methods
Evaluation of personal projects reports.
Oral exam, partly based on the projects reports.
Oral exam, partly based on the projects reports.
Bibliography
Des diapositives de cours et des documents supplémentaires sur les sujets traités sont disponibles sur le site MoodleUCL de l’unité d’enseignement.
Course slides and additional documents on the subjects addressed are available on the MoodleUCL website of the teaching unit.
Course slides and additional documents on the subjects addressed are available on the MoodleUCL website of the teaching unit.
Faculty or entity
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Physics
