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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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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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03/01/2022
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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09/26/2021
Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
The Large Hadron Collider (LHC) at the European Organisation for Nuclear...
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07/27/2021
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
We present an application of anomaly detection techniques based on deep ...
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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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05/03/2021
The Tracking Machine Learning challenge : Throughput phase
This paper reports on the second "Throughput" phase of the Tracking Mach...
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03/11/2021
Physics and Computing Performance of the Exa.TrkX TrackML Pipeline
The Exa.TrkX project has applied geometric learning concepts such as met...
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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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12/03/2020
Distributed Training and Optimization Of Neural Networks
Deep learning models are yielding increasingly better performances thank...
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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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10/05/2020
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
We present a fast simulation application based on a Deep Neural Network,...
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06/30/2020
Track Seeding and Labelling with Embedded-space Graph Neural Networks
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project ...
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05/04/2020
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to ...
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12/14/2019
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
Using detailed simulations of calorimeter showers as training data, we i...
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08/13/2019
Quantum adiabatic machine learning with zooming
Recent work has shown that quantum annealing for machine learning (QAML)...
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11/26/2018
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we de...
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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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06/29/2018
Topology classification with deep learning to improve real-time event selection at the LHC
We show how event topology classification based on deep learning could b...
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12/16/2017