Machine learning re-analysis of MINOS/MINOS+ neutrino oscillation data

Neutrino oscillations are being studied around the world, with new experiments being planned to come online in the next decade. The existing experiments are struggling to make improved measurements owing to the very low event rate measured in the detectors. This is because neutrinos have a very low interaction probability, and for that reason the detectors have to be very large. After the experiments have been running for some years, the statistical improvement year on year becomes very modest, and radical analysis improvements are called for to make better measurements. To this end, the existing experiments have already incorporated machine learning in their analysis. Furthermore, experiments are pooling their neutrino events to try to get more reach on the precision of the oscillation parameters.

MINOS was a the gold-standard long baseline experiment which took data between 2006 and 2016. Its results are presently being combined with those of the NOVA experiment to improve oscillation parameter precision accuracy. However, the MINOS data has not been subject to any machine learning improvements and is ripe for this kind of treatment to improve its precision.

The project would entail using existing MonteCarlo and Data samples, upgrading the analysis and storage of that data to use modern tools, and studying the improvements in the event reconstruction that can be achieved using modern machine learning techniques. This would likely result in a new publication within the time frame of the PhD.