We present an FPGA implementation of a Normalizing Flow (NF) for Anomaly Detection (AD) of new physics in realistic high-rate trigger systems of large HEP experiments. To the best of our knowledge, this marks the first operation of such an algorithm on FPGA, demonstrating anomaly detection performance and latency comparable to existing FPGA-based ML solutions.
We train a continuous NF model...
Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition...
We present a method to suppress pileup and calibrate hadronic jet energy at L1 triggers using boosted decision trees for regression and classification. The fwX platform is used for implementation of BDTs on FPGA within the necessary timing and resource constraints. The in-situ pileup suppression can improve trigger performance in the high pileup environment of the HL-LHC.
The upcoming high-luminosity upgrade to the LHC will involve a dramatic increase in the number of simultaneous collisions delivered to the Compact Muon Solenoid (CMS) experiment. To deal with the increased number of simultaneous interactions per bunch crossing as well as the radiation damage to the current crystal ECAL endcaps, a radiation-hard high-granularity calorimeter (HGCAL) will be...
