Set-Conditional Set Generation for Particle Physics
arXiv:2211.06406 · doi:10.1088/2632-2153/ad035b
Abstract
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.
arXiv comment10 pages, 9 figures
References in corpus (17)
- Adam: A Method for Stochastic Optimization
- Auto-Encoding Variational Bayes
- A Brief Introduction to PYTHIA 8.1
- CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
- Object-Centric Learning with Slot Attention
- How to GAN LHC Events
- AtlFast3: the next generation of fast simulation in ATLAS
- Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
- A Living Review of Machine Learning for Particle Physics
- MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
- Reconstructing particles in jets using set transformer and hypergraph prediction networks
- Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
- Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
- Machine Learning for Particle Flow Reconstruction at CMS
- Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
- Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
- Graph Generative Models for Fast Detector Simulations in High Energy Physics