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Teaching

Lecture course: Experimental particle physics

Winter semester · MSc level

A lecture course covering the experimental techniques and key measurements at the Large Hadron Collider. Topics include:

  • Accelerator physics and the LHC machine
  • Particle detection: tracking, calorimetry, muon systems
  • Trigger and data acquisition
  • Statistical methods for particle physics
  • Standard Model measurements at the LHC
  • Searches for physics beyond the Standard Model

Exercises are provided weekly, combining pen-and-paper problems with hands-on Python analysis tasks using open LHC data.

Materials and exercise sheets will be posted here at the start of the semester.

Advanced seminar: Top quark physics and quantum information

Summer semester · MSc level

A reading seminar exploring the intersection of top quark physics and quantum information theory. Students present and discuss recent papers on:

  • Spin correlations and entanglement in \(t\bar{t}\) production
  • Bell inequalities at colliders
  • Quantum state tomography with top quarks
  • Effective Field Theory interpretations of spin observables
  • Connections to quantum computing and quantum simulation

The seminar is suitable for students with a background in particle physics or quantum mechanics. Each participant gives one 45-minute presentation followed by group discussion.

Schedule and paper list will be posted at the start of the semester.

BSc projects for interested students

I regularly supervise BSc thesis projects. Below are examples of topics available for the current or upcoming academic year. If you are interested, please get in touch.

Monte Carlo simulation of rare top processes

Simulate \(t\bar{t}Z\) or \(tZq\) events using MadGraph5 and Pythia8, and compare generator-level predictions for key observables. Suitable for students with basic programming experience.

Skills you'll learn: Monte Carlo event generation, particle physics phenomenology, Python data analysis, ROOT.

Machine learning for jet classification

Train a neural network to distinguish \(b\)-quark jets from light-quark jets using simulated ATLAS data. Explore different architectures (dense, convolutional, graph-based) and compare their performance.

Skills you'll learn: Machine learning (PyTorch or TensorFlow), jet physics, classification metrics, data preprocessing.

Quantum entanglement observables in Higgs boson decays

Study the feasibility of measuring quantum entanglement in \(H \to ZZ^* \to 4\ell\) decays using Monte Carlo simulation. Compute entanglement witnesses and estimate the sensitivity with the expected HL-LHC dataset.

Skills you'll learn: Quantum information theory, Higgs physics, statistical analysis, Monte Carlo methods.