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 Convenors: Dr Eimear Conroy and Prof Todd Huffman
Eimear is a post-doctoral researcher in the University of Oxford’s Department of Physics, working with the ATLAS experiment at the Large Hadron Collider (LHC). She completed my DPhil (PhD) at Oxford, which included a 15-month placement at CERN, and I hold a BSc (Hons) in Physics from University College Dublin. Her research uses high-energy proton collisions to study the building blocks of matter and the forces that act between them. She focuses on precise measurements of how often different particle interactions occur, which tells us more about the Standard Model — our best description of the particles that make up the universe. In addition, she studies the structure of the proton itself, using ATLAS data to improve how we model and interpret collisions at the LHC. Alongside her research, she teaches quantum mechanics to undergraduates and courses on scientific communication and quantum chromodynamics (QCD) to graduate students. She also helps lead award-winning outreach work, including creating a programme that trains secondary school students to analyse real data from ATLAS.
Todd 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 or Online course on the following dates:
Session 2: 20th July - 7th August 2026