• To revise Python programming skills and reinforce object-oriented concepts and methods of a high-level Object-oriented programming language.

• To apply Python for the computational modelling of physical phenomena and solution of complex physics problems using Monte Carlo techniques and numerical integration.

• To further develop the ability to efficiently implement algorithms using Python and verify the results.

• To give students experience of working independently and in small groups on an original problem.

• To give students an opportunity to display the high quality of their work, initiative and ingenuity.

• To give students experience of report writing displaying high standards of composition and production.

• To give an opportunity for students to further develop and display oral communication skills.

(LO1) Acquire a deep knowledge of a high level programming language including object-oriented elements.

(LO2) Gain experience how to apply computational methods to the solution of physics problems, including the set up of a complex model of physical phenomena or experimental situation

(LO3) Experience in researching literature and other sources of relevant information

(LO4) Experience in testing model against data from experiment or literature

(LO5) Improved ability to organise and manage time.

(LO6) Improved skills in report writing.

(LO7) Improved skills in explaining project under questioning.

(S1) Problem solving skills

(S2) Teamwork

(S3) Organisational skills

(S4) Communication skills

(S5) IT skills

• Week 1: Introduction into the module; Revision of the Python programming techniques studied in Computational Physics (Phys205), including data structures, program flow, data input/output and plotting methods; Introduction of object-oriented concepts and application in an example with Lorentz Four-vectors and relativistic calculations

• Week 2: Revision of Monte Carlo Techniques: integration problems, random number generation (such as Gaussian or exponential distributions), application to simple physics problems; data handling and visualisation through histograms

• Week 3: Numerical solutions to differential equations with Euler and Runge-Kutta methods; application and verification of numerical solutions to the propagation of charged particles in electromagnetic fields

• Week 4: Construction of an object-oriented framework to construct experiments with particle tracking, simulated detection and reconstruction

• Week 5: Intr oduction of realistic interactions of particles with matter, implementation of effects of energy loss through ionisation and multiple scattering in the framework

• Week 6 - 10: Students will form groups of 3 to 4 and choose a project from a list suggested by the Module Organiser or prepared in discussion with the Module Organiser. Projects will be chosen to match the student's particular interests as far as possible. Details of the project aims will be decided in discussions between the student and the supervisor. An electronic logbook will be kept by the students to aid the team communication and improve possibilities to interact between students and supervisor. There will be regular meetings between the students and the supervisors to track the progress.

• The Projects are assessed by oral presentations, normally taking place in Week 11 of Semester 2, as well as a joint project report, normally to be handed in before the end of Week 11 of Semester 2. Th e electronic logbook will form a part of the assessment.

Teaching Method 1 - Lecture [online]
Description: 1 hour lectures to introduce students to weekly exercises
Attendance Recorded: Yes
Notes: 6 x 1 hour lectures

Teaching Method 2 - Laboratory Work
Description: Computing based activities
Attendance Recorded: Yes
Notes: 5 x 8 hours problems classes, 5 x 9 hours project work

The module will be delivered remotely in 2021. Asynchronous learning materials (notes/videos/exercises etc) will be made available to students through the VLE. The module will have regular synchronous sessions in active learning mode.
We are planning no changes to module content compared to previous years, and expect students to spend a similar amount of time-on-task compared to previous years. These changes will mainly constitute a rebalancing of hours from scheduled directed learning hours to unscheduled directed learning hours as students will have some flexibility as to when they access asynchronous materials.