We still have a possible PhD studentship available to start in 2017. The specific project with Prof. Ryan Nichol is described below, follow this link for how to apply.
Neutrinos in liquid argon detectors
Neutrinos, the most enigmatic of the fundamental particles, may hold the key to understanding why we live in a matter dominated Universe. The next generation of neutrino oscillation experiments will attempt to measure the fundamental differences between neutrinos and antineutrinos. In order to do this the experiments require enormous, exquisitely sensitive, detectors and immensely powerful neutrino beams.
A leading next generation experiment is the Deep Underground Neutrino Experiment, DUNE, which will utilise liquid-argon time-projection chamber detectors in a neutrino beam from Fermilab. The student will work on the commissioning, operation and data analysis of the prototype DUNE detectors, which will operate in a test beam at CERN. The results from these measurements are critical for optimising the design of the DUNE experiment and maximising its scientific potential. The student will have the opportunity to develop a broad range of skills: from constructing and testing hardware components, to detector simulation to developing particle identification and analysis tools using the latest advances in machine learning. There are also opportunities for further analysis of data from existing neutrino experiments.
For further information contact Prof. Ryan Nichol.
Identifying neutrinos using computer vision
A neutrino interaction produces a variety of charged particles, each of which leaves a record of its passage through a detector. In both liquid argon time projection chambers and highly segmented scintillator detectors such as NOvA, the particle tracks can be used to form images. These may be fed into existing deep learning algorithms, such as a Convolutional Neural Network (CNN), with the goal being to classify the interaction among the various neutrino “flavours”.
For further information contact Dr Chris Backhouse