Neural Network Algorithms for the CMS Level-1 Muon Trigger

The CMS trigger system selects interesting events from collision events in the LHC to keep the stored data to manageable size. The level-1 trigger is custom electronics designed to trigger events online very quickly and efficiently to meet tight timing requirements of microseconds to reach a decision. The current endcap muon trigger uses an external memory look-up table for the momentum assignment, and a trained Boosted Decision Tree for the algorithm. In preparation for the High Luminosity LHC, further reduction in the trigger rate is necessary in order to maintain similar thresholds in harsher conditions (higher pile-up). A more accurate assignment of the transverse momentum of muons reduces the rate coming from mismeasured lower momentum muons. Neural Networks can use a larger number of features to improve the momentum assignment and implementing neural nets directly in the FPGA logic without external memory frees us from the limitation on the number of address bits for a look-up table. I have investigated optimizing existing Neural Networks architecture. I’ll show performances for the pT resolution and the trigger rate and the optimization result for the architecture model including a robustness study of the algorithm.