Per le tesi specialistiche sono possibili periodi di trasferta a PSI.
Development of machine learning algorithms for the MEG-II drift chamber pattern recognition
The pattern recognition task is aimed to reconstruct positron tracks starting from
the energy deposits (hits) in each drift chamber cell.
The currently implemented algorithm of the MEG-II experiment is based on the search
"seeds" of hits in the external layers (with lower rate) that are subsequently merged to form tracks taking into account the magnetic field.
The purpose of the proposed thesis is to study the feasibility of substituting the above standard algorithm with a machine learning algorithm that
makes uses of deep neural networks (deep learning) and in the identification of the best algorithm for this purpose.
Research of the dark photon with the MEG-II detector
The Cockroft-Walton accelerator utilized for the liquid Xenon calibration can be used
to verify the presence of an excess of events at a mass of 16.7 MeV observed at th
e ATOMKI laboratory. This excess could be interpreted as a dark photon. The thesis cons
ists in the optimization of the experimental setup on the basis of simulations, in
the realization of an ultra-thin target for the CW and in the participation to the measurement in 2019.
Research of charged lepton flavor violation with charged muons
Further information can be asked to a group member