STI Publications - View Publication Form #17146
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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 |
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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 |
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Issue | |
Page(s) |
Attachments/Datasets/DOI Link
Document(s) |
GEMTRD_RT23_Article_v21.pdf
(STI Document)
TNS3259436_Accepted.pdf
(Accepted Manuscript)
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DOI Link | |
Dataset(s) | (none) |
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