Nanosecond hardware regression trees in FPGA at the LHC

Serhiayenka, Pavel; Roche, Stephen T.; Carlson, Benjamin T.; Hong, Tae Min

doi:10.1016/j.nima.2025.170209

Title: Nanosecond hardware regression trees in FPGA at the LHC

Journal Article · 28 February 2025 · Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

DOI: https://doi.org/10.1016/j.nima.2025.170209 · OSTI ID:2504252

Serhiayenka, Pavel;

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Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

OSTI ID:: 2504252

Journal Information:: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, Journal Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment Journal Issue: C Vol. 1072; ISSN 0168-9002

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (15)

Progress in resistive plate counters Cardarelli, R.; Santonico, R.; Di Biagio, A. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 263, Issue 1 https://doi.org/10.1016/0168-9002(88)91011-X	journal	January 1988
Fast muon tracking with machine learning implemented in FPGA Sun, Chang; Nakajima, Takumi; Mitsumori, Yuki Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1045 https://doi.org/10.1016/j.nima.2022.167546	journal	January 2023
Search for invisible decays of a Higgs boson produced through vector boson fusion in proton-proton collisions at s = 13 TeV Sirunyan, A. M.; Tumasyan, A.; Adam, W. Physics Letters B, Vol. 793 https://doi.org/10.1016/j.physletb.2019.04.025	journal	June 2019
Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays Roche, S. T.; Bayer, Q.; Carlson, B. T. Nature Communications, Vol. 15, Issue 1 https://doi.org/10.1038/s41467-024-47704-8	journal	April 2024
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider Govorkova, Ekaterina; Puljak, Ema; Aarrestad, Thea Nature Machine Intelligence, Vol. 4, Issue 2 https://doi.org/10.1038/s42256-022-00441-3	journal	February 2022
Development of FPGA-based neural network regression models for the ATLAS Phase-II barrel muon trigger upgrade Ospanov, Rustem; Feng, Changqing; Dong, Wenhao EPJ Web of Conferences, Vol. 251 https://doi.org/10.1051/epjconf/202125104031	journal	January 2021
Software and firmware co-development using high-level synthesis Ghanathe, N. P.; Madorsky, A.; Lam, H. Journal of Instrumentation, Vol. 12, Issue 01 https://doi.org/10.1088/1748-0221/12/01/C01083	journal	January 2017
Fast inference of deep neural networks in FPGAs for particle physics Duarte, J.; Han, S.; Harris, P. Journal of Instrumentation, Vol. 13, Issue 07 https://doi.org/10.1088/1748-0221/13/07/P07027	journal	July 2018
Fast inference of Boosted Decision Trees in FPGAs for particle physics Summers, S.; Guglielmo, G. Di; Duarte, J. Journal of Instrumentation, Vol. 15, Issue 05 https://doi.org/10.1088/1748-0221/15/05/P05026	journal	May 2020
Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics Hong, T. M.; Carlson, B. T.; Eubanks, B. R. Journal of Instrumentation, Vol. 16, Issue 08 https://doi.org/10.1088/1748-0221/16/08/P08016	journal	August 2021
Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics Carlson, B. T.; Bayer, Q.; Hong, T. M. Journal of Instrumentation, Vol. 17, Issue 09 https://doi.org/10.1088/1748-0221/17/09/P09039	journal	September 2022
Testing a Neural Network for Anomaly Detection in the CMS Global Trigger Test Crate during Run 3 Zipper, Noah Journal of Instrumentation, Vol. 19, Issue 03 https://doi.org/10.1088/1748-0221/19/03/C03029	journal	March 2024
LHC Machine Evans, Lyndon; Bryant, Philip Journal of Instrumentation, Vol. 3, Issue 08 https://doi.org/10.1088/1748-0221/3/08/S08001	journal	August 2008
Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades Ospanov, Rustem; Feng, Changqing; Dong, Wenhao The European Physical Journal C, Vol. 82, Issue 6 https://doi.org/10.1140/epjc/s10052-022-10521-8	journal	June 2022
Development of the ATLAS Liquid Argon Calorimeter Readout Electronics and Machine Learning for the HL-LHC Gonski, Julia Instruments, Vol. 6, Issue 3 https://doi.org/10.3390/instruments6030028	journal	August 2022

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