Tracking aware metric learning for particle reconstruction

We propose a model for learning a metric that simplifies the finding of particle trajectories. A new learning process is designed such that geometrical and clustering constraints from spatial measurements are incorporated. The network allows to map any set of input points into a new space where traces produced by the same particle are clustered. The approach is experiment agnostic and results are demonstrated on the TrackML dataset.