Hashing and similarity learning for tracking with the HL-LHC ATLAS detector

At the High Luminosity Large Hadron Collider (HL-LHC), up to 200 proton-proton collisions happen during a single bunch crossing. This leads on average to tens of thousands of particles emerging from the interaction region. The CPU time of traditional approaches of constructing hit combinations will grow exponentially as the number of simultaneous collisions increases at the HL-LHC, posing a major challenge. A framework for similarity hashing and learning for track reconstruction will be described where multiple small regions of the detector, referred to as buckets, are reconstructed in parallel within the ATLAS simulation framework. New developments based on metric learning for the hashing optimisation will be introduced and new results obtained both with the TrackML dataset [1] as well as ATLAS simulation will be presented. [1] Rousseau. D, et al. "The TrackML challenge." 2018.