As the LHC luminosity is ramped up to 3 × 10 34 cm 22s 1 and beyond, the high rates, multiplicities, and energies of particles seen by the detectors will pose a unique challenge. Only a tiny fraction of the produced collisions can be stored offline and immense real-time data reduction is needed. An effective trigger system must maintain high trigger efficiencies for the physics we are most interested in while suppressing the enormous QCD backgrounds. This requires massive computing power to minimize the online execution time of complex algorithms. A multi-level trigger is an effective solution to meet this challenge. The Fast Tracker (FTK) is an upgrade to the current ATLAS trigger system that will operate at full Level-1 output rates and provide high-quality tracks reconstructed over the entire inner detector by the start of processing in the Level-2 Trigger. FTK solves the combinatorial challenge inherent to tracking by exploiting the massive parallelism of associative memories that can compare inner detector hits to millions of pre-calculated patterns simultaneously. The tracking problem within matched patterns is further simplified by using pre-computed linearized fitting constants and relying on fast DSPs in modern commercial FPGAs. Overall, FTK is able to compute the helix parameters for all tracks in an event and apply quality cuts in less than 100 μs. The system design is defined and the performance presented with respect to high transverse momentum (high-p T) Level-2 objects: b jets, tau jets, and isolated leptons. We test FTK algorithms using the full ATLAS simulation with WH events up to 3×10 34cm 2s 1 luminosity and compare the FTK results with the offline tracking capability. We present the architecture and the reconstruction performance for the mentioned high-p T Level-2 objects.
- Associative memory
- particle tracking
- pattern recognition
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Nuclear Energy and Engineering
- Nuclear and High Energy Physics