63 citations · 298 across the 25 of their papers we have counts for
25 papers
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
Shi-Yu Huang, Yun-Chen Yang, Yu-Ru Su +6
In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) o…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Learning to Identify Semi-Visible Jets
Taylor Faucett, Shih-Chieh Hsu, Daniel Whiteson
We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information…
Data Science and Machine Learning in Education
Gabriele Benelli, Thomas Y. Chen, Javier Duarte +20
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sop…
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima +10
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environme…
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott +11
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce s…
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
Hendrik Borras, Giuseppe Di Guglielmo, Javier Duarte +15
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml an…
Exploring the Universality of Hadronic Jet Classification
Kingman Cheung, Yi-Lun Chung, Shih-Chieh Hsu +1
The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on diff…
Probing highly collimated photon-jets with deep learning
Xiaocong Ai, Shih-Chieh Hsu, Ke Li +1
Many extensions of the standard model (SM) predict the existence of axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV particles, a scalar and a pseudosca…
Physics Community Needs, Tools, and Resources for Machine Learning
Philip Harris, Erik Katsavounidis, William Patrick McCormack +18
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this w…
Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
Chun-Yi Wang, Xiangyang Ju, Shih-Chieh Hsu +22
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalm…
Recent Progress and Next Steps for the MATHUSLA LLP Detector
Cristiano Alpigiani, Juan Carlos Arteaga-Velázquez, Austin Ball +93
We report on recent progress and next steps in the design of the proposed MATHUSLA Long Lived Particle (LLP) detector for the HL-LHC as part of the Snowmass 2021 process. Our under…
Accelerating the Inference of the Exa.TrkX Pipeline
Alina Lazar, Xiangyang Ju, Daniel Murnane +21
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.Tr…
Graph Neural Networks for Charged Particle Tracking on FPGAs
Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang +12
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction…
SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
Alexander Shmakov, Michael James Fenton, Ta-Wei Ho +3
The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce…
Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
Jakub Filipek, Shih-Chieh Hsu, John Kruper +2
With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model…
Quantum Machine Learning with SQUID
Alessandro Roggero, Jakub Filipek, Shih-Chieh Hsu +1
In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical…
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig +27
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.…
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
Xiangyang Ju, Daniel Murnane, Paolo Calafiura +21
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detecto…
FPGAs-as-a-Service Toolkit (FaaST)
Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris +13
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically a…
Disentangling Boosted Higgs Boson Production Modes with Machine Learning
Yi-Lun Chung, Shih-Chieh Hsu, Benjamin Nachman
Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum () are sensitive probes of physics beyond the Standard Model. However, high Higgs Boson…
GPU coprocessors as a service for deep learning inference in high energy physics
Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas +13
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limi…
FPGA-accelerated machine learning inference as a service for particle physics computing
Javier Duarte, Philip Harris, Scott Hauck +20
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large pote…
Studies of Vector Boson Scattering And Triboson Production with DELPHES Parametrized Fast Simulation for Snowmass 2013
C. Degrande, J. L. Holzbauer, S. -C. Hsu +9
Multiboson production provides a unique way to probe Electroweak Symmetry Breaking (EWSB) and physics beyond the Standard Model (SM). With the discovery of the Higgs boson, the def…