Measurement of the neutrino oscillation parameters using Bayesian Markov Chain Monte Carlo at T2K

Neutrinos are the most abundant matter particle in the Universe, but very little is known about them as they only interact weakly. For a long time it had been assumed that neutrinos would be massless, but since roughly 20 years, there has been mounting evidence that the different kind of neutrinos can transform themselves from one flavour to another. The discovery of this is process called neutrino oscillations as they go forth and back between the different flavours was awarded with the Nobel Prize in Physics in 2015, which was awarded to Takaaki Kajita and Arthur B. McDonald using the SuperKamiokande and SNO experiments respectively.

While these experiments were using natural not very well understood neutrino sources, T2K experiment has gone a step further. I beam of muon neutrinos is produced in a controlled environment at the J-PARC accelerator centre on the east coast of Japan and is directed to the SuperKamiokande detector. The beam is measured twice, once in a near detector 280m away from the neutrino production target before the neutrinos could change and once with the SuperKamiokande detector 300 km downstream. Comparing the measurements of both detectors allows a precision determination of the oscillation parameters. Furthermore, T2K has made these measurements with both neutrinos and anti-neutrinos and this lead to some first indications that the oscillations are not the same and thus violate the CP-Symmetry. This means that matter and anti-matter do not behave the same and may eventually help to explain the matter anti-matter asymmetry of the universe, which is entirely matter dominated.

The thesis will be performed as part of the international T2K collaboration, which has around 500 members from around the globe. The aim of the thesis is to improve the measurement of the parameters governing neutrino oscillations by taking additional data to reduce the statistical uncertainty and, equally important, to reduce the systematic uncertainties of the measurement.

The student will use one of the T2K Oscillation analysis teams and develop new methods that better treat the systematic errors or reduces them and apply these to existing and new data sets. The methods will need to be carefully verified and the results compared to those of alternative analysis teams. It is likely that a Markov Chain Monte Carlo approach will be used to find optima in a highly dimensional parameter space. This has not yet been done using the near detector data and would allow for a more consistent treatment of correlated and uncorrelated uncertainties and a simultaneous fit to the near and far detector data leading to an improved sensitivity to CP-violating effects.