Iterative Retina for High Track Multiplicity in a Barrel-Shaped Tracker and High Magnetic Field

Real-time particle track reconstruction in high-energy physics experiments at colliders running at high luminosity is very challenging for trigger systems. To perform pattern recognition and track fitting in online trigger system, the artificial Retina algorithm has been introduced in the field. Retina can be implemented in the state-of-the-art field-programmable gate array (FPGA) devices. Our developments use Retina in an iterative way to identify tracks in a barrel-shaped tracker embedded in a high magnetic field and with high track multiplicity. As a benchmark, we simulate LHC t-tbar events from 14-TeV proton-proton collisions, with a pile-up of 200 interactions. The produced particles are propagated using GEANT-4 in a 4-T magnetic field from the interaction point through a six-layer barrel tracker made of silicon modules. With this sample, the performance of the hardware design [FPGA resource usage and latency] is evaluated. Both track reconstruction efficiency and purity of the Retina track finding are over 90%. To improve further the resolution on the track parameters, we are investigating the addition of a Kalman filter process on the FPGA, after the Retina step. First results obtained with an emulator of the Kalman filter are also discussed in this article.