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09/13/2023
Scalable neural network models and terascale datasets for particle-flow reconstruction
We study scalable machine learning models for full event reconstruction ...
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06/20/2023
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
In-time particle trajectory reconstruction in the Large Hadron Collider ...
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03/30/2023
Progress towards an improved particle flow algorithm at CMS with machine learning
The particle-flow (PF) algorithm, which infers particles based on tracks...
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12/14/2022
Lorentz Group Equivariant Autoencoders
There has been significant work recently in developing machine learning ...
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12/09/2022
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data princi...
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11/18/2022
On the Evaluation of Generative Models in High Energy Physics
There has been a recent explosion in research into machine-learning-base...
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11/17/2022
Do graph neural networks learn traditional jet substructure?
At the CERN LHC, the task of jet tagging, whose goal is to infer the ori...
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09/26/2022
FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs
Steganography and digital watermarking are the tasks of hiding recoverab...
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07/19/2022
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in high-...
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03/23/2022
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Many physical systems can be best understood as sets of discrete data wi...
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03/01/2022
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation ...
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03/01/2022
Machine Learning for Particle Flow Reconstruction at CMS
We provide details on the implementation of a machine-learning based par...
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11/24/2021
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Autoencoders have useful applications in high energy physics in anomaly ...
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11/24/2021
Explaining machine-learned particle-flow reconstruction
The particle-flow (PF) algorithm is used in general-purpose particle det...
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06/22/2021
Particle Cloud Generation with Message Passing Generative Adversarial Networks
In high energy physics (HEP), jets are collections of correlated particl...
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01/21/2021
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
In general-purpose particle detectors, the particle flow algorithm may b...
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11/30/2020
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse dat...
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08/08/2020
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance f...
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07/20/2020
GPU coprocessors as a service for deep learning inference in high energy physics
In the next decade, the demands for computing in large scientific experi...
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03/11/2020
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We present the implementation of binary and ternary neural networks in t...
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02/05/2020
Fast inference of Boosted Decision Trees in FPGAs for particle physics
We describe the implementation of Boosted Decision Trees in the hls4ml l...
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07/08/2018
Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
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04/16/2018