TrickTrack: An experiment-independent, cellular- automaton based track seeding library

The design of next-generation particle accelerators evolves to higher and higher luminosities, as seen in the HL-LHC upgrade and the plans for the Future Circular Collider (FCC). Writing track reconstruction software that can cope in  these high-pileup scenarios is challenging due to the inherent complexity of current algorithmic approaches. In this contribution we present TrickTrack, a track reconstruction toolkit based on the cellular automaton-based algorithm used for track seeding in the CMS experiment. It is a concurrency-friendly implementation of an algorithm for pattern recognition problems, and tries to remain general enough to be of use in most tracking detectors. The performance of TrickTrack in the FCC-hh design study, which features pileup rates of 1000 interactions per bunch crossing and a high-occupancy environment for tracking, is presented as the first use case beyond CMS.