Track Finding with Graph Neural Network classification in the MEG II Experiment

We present the implementation and performance
of a Graph Neural Network (GNN) hit classifier applied to
boost the performances of the Track Finder algorithm of the
MEG II experiment, improving positron tracking capabilities at
high beam intensities. The algorithm classifies detector hits using
a heterogeneous graph neural network architecture that processes
hits from both the cylindrical drift chamber (CDCH) and the
pixelated timing counter (pTC), the subdetectors composing the
MEG II spectrometer. This novel track finder algorithm achieves
a higher tracking efficiency at all beam intensities, as well as a
better resolution on kinematic observables, allowing to improve
the experimental sensitivity on the µ+ → e+ γ search.