End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

The high-luminosity upgrade of the Large Hadron Collider protons are brought to collision; to increase the probability (HL-LHC) will present unprecedented computing challenges of rare and interesting interactions (e.g., the production [1]. Because the processing complexity of LHC collision of a Higgs boson) to occur. Because of this, a single scales with the number of hits and energy deposits in collision event contains the particles resulting from more the detectors from interacting particles, the computing resource than one collision (the primary particles). These particles needs will increase significantly as a function of the travel through the detector components and, when crossing number of simultaneous proton-pair collisions at each particle a calorimeter, produce showers of other particles (secondary beam crossing (pileup).