T. M. Hong

Physics and Astronomy · Indiana University

Particle physics theoretical and experimental studies High-Energy Particle Collisions Research Quantum Chromodynamics and Particle Interactions Particle Detector Development and Performance Dark Matter and Cosmic Phenomena

Publications

1,861

Citations

100,715

Est. group size

—

Recurring co-author estimate

Active years

Publishing since 1998

Research summary

AI-generated

T. M. Hong works in experimental particle physics, studying high-energy collisions such as those at the Large Hadron Collider (LHC) and searching for rare processes like unusual decays of the Higgs boson. A major focus is developing fast machine-learning methods (for example, decision trees) implemented directly on specialized hardware chips called FPGAs, so that particle-collision data can be analyzed in nanoseconds for real-time trigger and detection systems.

Particle physics and Higgs boson decaysMachine learning on FPGA hardwareReal-time triggering and anomaly detectionParticle detector systems (ATLAS at the LHC)Exotic and hadronic final states

Publication activity has been relatively steady but modest over the last five years, averaging around two to three papers per year after a busier 2017.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence

Publications per year over the last 10 years — averaging 2.6/year recently

Recent publications

Nanosecond hardware regression trees in FPGA at the LHC
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · 2025
The Areal Typology of Languages of the Americas (ATLAs) database
Scientific Data · 2025
Decision tree-based anomaly detection on FPGA
2025
Ring-based ML calibration with in situ pileup correction for real-time jet triggers
arXiv (Cornell University) · 2025
Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
Nature Communications · 2024
Nanosecond hardware regression trees in FPGA at the LHC
arXiv (Cornell University) · 2024
Illuminating all-hadronic final states with a photon: Exotic decays of the Higgs boson to four bottom quarks in vector boson fusion plus gamma at hadron colliders
Physical review. D/Physical review. D. · 2024
Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
arXiv (Cornell University) · 2023
Signal and background samples for Higgs boson decays to four b-jets
Zenodo (CERN European Organization for Nuclear Research) · 2023
Illuminating all-hadronic final states with a photon: Exotic decays of the Higgs boson to four bottom quarks in vector boson fusion plus gamma at hadron colliders
arXiv (Cornell University) · 2023
Signal and background samples for Higgs boson decays to four b-jets
Zenodo (CERN European Organization for Nuclear Research) · 2023
Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics
Journal of Instrumentation · 2022
Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics
arXiv (Cornell University) · 2022
Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
Journal of Instrumentation · 2021
Study of Topocluster Position Resolution in Firmware for the Global Event Processor for the HL-LHC Upgrade of the ATLAS Trigger System
Bulletin of the American Physical Society · 2021

Publishes in

Bulletin of the American Physical Society×11
arXiv (Cornell University)×8
Physical review. D/Physical review. D.×4
CERN Document Server (European Organization for Nuclear Research)×4
Journal of Instrumentation×2

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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