Publications
Publication Information
Title | Development of ML FPGA filter for particle identification and tracking in real time |
Authors | Fernando Barbosa, Cody Dickover, Sergey Furletov, David Lawrence, Dmitry Romanov, Lee Belfore, Cristiano Fanelli, Denis Furletov, Jokhovets Lioubov, Nathan Branson |
JLAB number | JLAB-PHY-22-3711 |
LANL number | (None) |
Other number | DOE/OR/23177-5606 |
Document Type(s) | (Journal Article) |
Associated with EIC: | No |
Supported by Jefferson Lab LDRD Funding: | No |
Funding Source: | Nuclear Physics (NP) |
Journal Compiled for IEEE Transactions on Nuclear Science Refereed | |
Publication Abstract: | Real-time data processing is a frontier field in experimental particle physics. Machine Learning methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern FPGA boards allows us to add more sophisticated algorithms for real time data processing. Many tasks could be solved using modern Machine Learning (ML) algorithms which are naturally suited for FPGA architectures. The FPGA-based machine learning algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich data sets for event selection. Work has started to develop an FPGA based Machine Learn- ing (ML) algorithm for a real-time particle identification and tracking with Transition Radiation detector and E/M Calorimeter. |
Experiment Numbers: | other |
Group: | Hall D |
Document: | |
DOI: | http://dx.doi.org/10.1109/TNS.2023.3259436 |
Accepted Manuscript: | TNS3259436_Accepted.pdf |
Supporting Documents: | |
Supporting Datasets: |