Reconstructing thousands of particles in one go at the CERN LHC with TensorFlow

Detecting and measuring particles

For this purpose, the collision interaction points are surrounded by large detectors covering as much as possible in all possible directions and energies of the decay products. These detectors are further split into sub-detectors, each collecting complementary information. The innermost detector, closest to the interaction point, is the tracker consisting of multiple layers. Similar to a camera, each layer can detect the spatial position at which a charged particle passed through it, providing access to its trajectory. Combined with a strong magnetic field, this trajectory gives access to the particle charge and the particle momentum.

While the tracker is aimed at measuring the trajectories, only, while minimising any further interaction with and scattering of the particles, the next sub-detector layer is aimed at stopping them entirely. By stopping the particles completely, these calorimeters can extract the initial particle energy, and can also detect neutral particles. The only particles that pass through these calorimeters are muons, which are identified by additional muon chambers that constitute the outermost detector shell and use the same detection principles as the tracker.

Since many of the reconstruction tasks, even the refinement of already reconstructed particles, rely on an unknown number of inputs, the recent addition and support of , where object properties are condensed in at least one representative condensation point per object that can be chosen freely by the network through a high confidence score.