Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
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Abstract (arXiv)
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC. We find the model successfully generates sparse MNIST digits and particle jet data. We quantify agreement between real and generated data with a graph-based Fr'echet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2020.
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Citing papers per year: 2021: 6; 2022: 3; 2023: 2.
11 papers.
- 2021A Living Review of Machine Learning for Particle Physicscited by 78
- 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
- 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
- 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
- 2022Lorentz group equivariant autoencoderscited by 15
- 2021Latent Space Refinement for Deep Generative Modelscited by 10
- 2021Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physicscited by 9
- 2022Particle-based Fast Jet Simulation at the LHC with Variational Autoencoderscited by 7
- 2021Shared Data and Algorithms for Deep Learning in Fundamental Physicscited by 4
- 2023Versatile Energy-Based Probabilistic Models for High Energy Physicscited by 2
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Review15 Dec 2023
- 2023Versatile Energy-Based Probabilistic Models for High Energy Physics1 Feb 2023
- 2022Lorentz group equivariant autoencoders14 Dec 2022
- 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulation15 Mar 2022
- 2022Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders1 Mar 2022
- 2021Shared Data and Algorithms for Deep Learning in Fundamental Physics1 Jul 2021
- 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows9 Jun 2021
- 2021Latent Space Refinement for Deep Generative Models1 Jun 2021
- 2021Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics7 Apr 2021
- 2021A Living Review of Machine Learning for Particle Physics2 Feb 2021
- 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks21 Jan 2021