Module Catalogues

Nuclear and Particle Physics

Module Title Nuclear and Particle Physics
Module Level Level 3
Module Credits 5
Academic Year 2028/29
Semester SEM1

Aims and Fit of Module

This module aims to give students a broad introduction to nuclear and particle physics. It focuses on applying general concepts from quantum mechanics to the description of physical systems at the nuclear and subnuclear scales. In this sense, this module complements more theoretical modules in quantum mechanics encountered by students in the programme (PHY207 and PHY304), in the context of nuclei and other subatomic particles. At the same time, the module introduces new concepts that are specific to the modeling of particle interactions (e.g., Feynman diagrams). Finally, this module also highlights the practical and experimental applications of its theoretical concepts, such as in the description of radiation detectors, nuclear reactors, and particle accelerators.

Learning outcomes

A. apply quantum mechanics to describe nuclear structures B. explain the fundamentals of radiation interaction with matter C. predict outcomes in particle decays and reactions D. describe the operational principles of nuclear reactors and particle accelerators

Method of teaching and learning

This module is delivered over 13 teaching weeks in year 4, semester 1. Students are expected to attend four hours of formal lectures and tutorials in a typical week. During the lectures, students will be introduced to the academic content and practical skills underlying the module. Tutorials will focus on solving exercises in class, aiming to teach students how to apply lecture concepts in practical situations. In addition, students are required to dedicate 7-8 hours per week to independent study; this time should be used for background reading, reflection, and reviewing the lecture material. Students are encouraged to utilize AI tools and to critically explore the strengths and limitations of generative AI for enhancing their learning and comprehension, including understanding when generative AI works effectively and when it does not.