Conveners
Physics, Contributed 2
- Dylan Rankin (University of Pennsylvania)
We present decision trees designs that are optimized for FPGA in high energy physics trigger systems. Designs for classification, regression, anomaly detection, which occupy O(1)% resources and execute at 10s of nanoseconds, are presented. Four papers are summarized: Hong et al., JINST 16, P08016 (2021) http://doi.org/10.1088/1748-0221/16/08/P08016; Carlson et al., JINST 17, P09039 (2022)...
Central Drift Chamber in the Belle II experiment is one of the charged tracking device for not only offline but also real-time trigger systems. In the operation so far, we observe an issue of cross-talk noise in the Front-End Electronics device, where a bunch of noise wire hits happen in nearby regions. This issue causes fake tracks in hardware trigger and also increases the processing loading...
Integration of machine learning (ML) algorithms within the front-end ASICs used for charge detection and readout in high-energy and nuclear physics experiments can alleviate data transfer bottlenecks to back-end data acquisition systems. By only transmitting higher-level signal features inferred from the front-end signals (such as amplitude, time constant, time of arrival, or even particle...
