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06/22/2023
Triggering Dark Showers with Conditional Dual Auto-Encoders
Auto-encoders (AEs) have the potential to be effective and generic tools...
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05/23/2023
Goodness of fit by Neyman-Pearson testing
The Neyman-Pearson strategy for hypothesis testing can be employed for g...
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05/06/2023
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the feasibility of de...
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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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02/15/2023
Towards Optimal Compression: Joint Pruning and Quantization
Compression of deep neural networks has become a necessary stage for opt...
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01/25/2023
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Much hope for finding new physics phenomena at microscopic scale relies ...
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01/25/2023
Quantum anomaly detection in the latent space of proton collision events at the LHC
We propose a new strategy for anomaly detection at the LHC based on unsu...
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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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10/16/2022
FIT: A Metric for Model Sensitivity
Model compression is vital to the deployment of deep learning on edge de...
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09/28/2022
LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors
This work proposes a novel reconfigurable architecture for low latency G...
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05/16/2022
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
In this paper, we investigate how field programmable gate arrays can ser...
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04/05/2022
Learning new physics efficiently with nonparametric methods
We present a machine learning approach for model-independent new physics...
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04/04/2022
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
We present an end-to-end reconstruction algorithm to build particle cand...
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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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02/09/2022
Lightweight Jet Reconstruction and Identification as an Object Detection Task
We apply object detection techniques based on deep convolutional blocks ...
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12/06/2021
Autoencoders for Semivisible Jet Detection
The production of dark matter particles from confining dark sectors may ...
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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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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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06/26/2021
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
This paper presents novel reconfigurable architectures for reducing the ...
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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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10/12/2020
Anomaly Detection With Conditional Variational Autoencoders
Exploiting the rapid advances in probabilistic inference, in particular ...
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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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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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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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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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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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02/21/2019
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
We explore the use of graph networks to deal with irregular-geometry det...
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01/16/2019
LHC analysis-specific datasets with Generative Adversarial Networks
Using generative adversarial networks (GANs), we investigate the possibi...
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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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07/27/2018
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider
Reliable data quality monitoring is a key asset in delivering collision ...
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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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04/16/2018
