The particle-flow (PF) algorithm, which infers particles based on tracks...
The growing role of data science (DS) and machine learning (ML) in
high-...
We study how to use Deep Variational Autoencoders for a fast simulation ...
We provide details on the implementation of a machine-learning based par...
Autoencoders have useful applications in high energy physics in anomaly
...
The particle-flow (PF) algorithm is used in general-purpose particle
det...
The Large Hadron Collider (LHC) at the European Organisation for Nuclear...
We present an application of anomaly detection techniques based on deep
...
In high energy physics (HEP), jets are collections of correlated particl...
This paper reports on the second "Throughput" phase of the Tracking Mach...
The Exa.TrkX project has applied geometric learning concepts such as met...
In general-purpose particle detectors, the particle flow algorithm may b...
Deep learning models are yielding increasingly better performances thank...
We develop a graph generative adversarial network to generate sparse dat...
We present a fast simulation application based on a Deep Neural Network,...
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project ...
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to ...
Using detailed simulations of calorimeter showers as training data, we
i...
Recent work has shown that quantum annealing for machine learning (QAML)...
Using variational autoencoders trained on known physics processes, we de...
Machine learning is an important research area in particle physics, begi...
We show how event topology classification based on deep learning could b...
We present a lightweight Python framework for distributed training of ne...