Particle physics

CP Violation is the study of differences between matter and antimatter. At the big bang, we believe an equal amount of both must have been created, but today we only find evidence of matter in our universe. Some asymmetry must therefore occur in the laws of physics at higher energies. We study this at LHCb by creating high-energy collisions, and measuring the rates and interactions of matter and antimatter. This way, we hope to learn about the forces at play in the early universe, and why our universe looks the way it does, today.

Rare particle decays are an excellent way to study the possible existence of new, fundamental particles. Such new particles can have an imprint on properties such as decay rates of rare processes, through so-called ‘quantum loops’. LHCb is the world-leading expert in the search for new particles using rare decays of beauty baryons.

Charm physics: LHCb has collected worlds’ largest samples of particles containing a charm quark. By studying the production and asymmetries of these particles in the high-energy collisions at the LHC, we can learn something about the strong nuclear force that binds these quarks together.

Pattern recognition: In order to do physics, the raw data of the LHCb detector needs to be reconstructed: finding true particle tracks from individual readout ‘hits’. As this is run in real-time during the particle collisions (~40 million per second), this falls into the area of high-performance computing. At UM, we are deeply involved with these algorithms, and studying increased performance by using new architectures such as graphical processing units or quantum computers.

Machine learning: Artificial Intelligence or Machine Learning is a great help in extracting useful information from the large amounts of data generated by the particle collisions at the LHC. At MSP, we study the use of various of these tools: deep neural networks, convolutional networks, anomaly detection, long-short term memory, and more. These studies are performed in collaboration with experts at DKE.

One of the most interesting hints for new particles comes from the rare decays including electrons. Due to their light mass, however, these electrons lose a lot of energy in our detector, in the form of photons. This study aims to write a machine learning algorithm that finds these photons, and results in a better reconstruction of the electron energy.

• Student: Felix Quinque

Here we perform the full analysis of the production rate of Xic charm baryons at the high-energy collisions at the LHC. The resulting paper will be used by theorists to improve their models of the strong interaction.

• Students: Simon Calo, Jonas Tjepkema, honours project

In this study we explored the usage of anomaly detection algorithms to generically search for new, unexpected phenomena in the LHCb data.

• Student: Melissa Lopez

In this study we explored the addition of a new variable, the “time-of-flight”, to the neural network used for the identification of particles in LHCb.

• Students: Lizzy Rieth, Nelson Waakop Reijers

In this study, the trigger strategy for the very important Bs -> j/psi phi decays in 2021 were studied. The inclusion of beauty hadron identification (aka ‘flavour tagging’) parameters in the trigger can significantly enlarge the signal data rates for the near-future data taking at the upgraded LHCb detector.

• Student: Ali Sidley

In this study, students analysed recent data from the LHCb experiment that include charmed baryons. Various models were built and fitted to the invariant mass distributions in data, to study the effects of detector resolution and signal to background ratio in these reconstructed particles.

• Students: Joyce Cobussen, Jord Muffels, Diyon Wickremeratne, Francesco Marangio