Machine learning predictor for ‘measurement-to-track’ association for the ATLAS inner detector trigger

The track finding algorithm adopted for the LHC Run-2 data taking period is based on combinatorial track following, where the seed number scales non-linearly with the number of hits. The corresponding CPU time increase, close to cubical, creates huge and ever-increasing demand for computing power. This is particularly problematic for the silicon tracking detectors, where the hit occupancy is the largest. Therefore, it is essential that resource use is reduced, whilst maintaining the ability to reconstruct tracks with minimal loss in efficiency. This paper briefly summarises the work that has been done to optimise the HLT ID track seeding software for ATLAS Run-3 and beyond, in order to reduce the number of fake seed constructed. An ML-based algorithm has been developed to predict if a pair of hits belong to the same track given input hit features, focusing on cluster width and inverse track inclination. The implementation of the trained predictor in the form of Look-Up Tables is presented, where the resulting full-scan ID tracking efficiency and speed-up factor obtained using simulated data is also discussed.