STI Publications - View Publication Form #17146

Back to Search Results Print
Publication Information
Title Development of ML FPGA filter for particle identification and tracking in real time
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.
Author(s) Fernando Barbosa, Cody Dickover, Sergey Furletov , David Lawrence, Dmitry Romanov, Lee Belfore, Cristiano Fanelli , Denis Furletov , Jokhovets Lioubov, Nathan Branson
Publication Date March 2023
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Exp Nuclear Physics / Experimental Halls / Hall D
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-PHY-22-3711 OSTI Number: 1970496
LANL Number: Other Number: DOE/OR/23177-5606
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name IEEE Transactions on Nuclear Science
Refereed Yes
Volume
Issue
Page(s)
Attachments/Datasets/DOI Link
Document(s)
TNS3259436_Accepted.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
Back to Search Results Print