About this course
Join this course to explore the universe at the smallest distances and highest energies where quantum mechanics and special relativity collide.
Over the course of 3 weeks of lectures from world-experts and hands on seminars, students will join the particle physicists from the Large Hadron Collider's ATLAS experiment in analysing real proton collisions from the world’s largest machine - uncovering the secrets of the building blocks of the universe. In the practical component, participants will work through coding notebooks to learn the Python data analysis techniques used by professional particle physicists, progressing from the very basics, to rediscovering the Higgs boson, to culminating with an original analysis of real data. Along the way, expect to develop computer coding, big data analysis and machine learning skills that will carry over to the real world.
Course Convenor: Prof Todd Huffman
Prof Huffman is an experimental physicist in high energy particle physics. His current research projects are in association with the ATLAS experiment at the Large Hadron Collider at CERN near Geneva, Switzerland. Prior to coming to Oxford, he was a researcher with the University of Pittsburgh doing research at the Collider Detector Facility (CDF) at the Fermi National Accelerator Laboratory, where his research was searching for, and then finding and studying, the B_c meson (a particle consisting of the bound pairing of a bottom quark and a charm quark).
Learning Outcomes
- Understand and evaluate data; the methods by which it is gathered, analysed, and its limitations, particularly in the context of experimental particle physics.
- Demonstrate understanding nature of subatomic particles, of the physical processes by which they are produced, and the functioning of the experimental apparatus by which they are observed.
- Demonstrate understanding of the signal models, background estimation methods, experimental techniques used in experimental particle physics.
- Exhibit proficiency in reading and writing computer code, and familiarity with coding best practices.
- Demonstrate awareness of the advantages and limitations of machine learning in particle physics, and the ability to critically evaluate the ways in which it is used in broader society.
- Gain an appreciation for the historical context underlying the field of particle physics, and potential directions it may take in the future.
Who is this course suitable for?
This course is suitable for students with a strong interest in Physics, Data Science, or Computing who are eager to explore fundamental particles and forces through hands-on analysis of real data from the Large Hadron Collider.
Dates and availability
Available as a Residential course on the following dates:
Session 2: 19th July - 6th August 2027