Shared Data and Algorithms for Deep Learning in Fundamental Physics

Abstract

We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies. The datasets contain hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories. While public datasets from multiple fundamental physics disciplines already exist, the common interface and provided reference models simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. We discuss the design and structure and line out how additional datasets can be submitted for inclusion. As showcase application, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks. We show that our approach reaches performance close to dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.

arXiv comment14 pages, 3 figures, 5 tables - Version accepted by Computing and Software for Big Science

References in corpus (60)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Array Programming with NumPy
• Semi-Supervised Classification with Graph Convolutional Networks
• Big Bang Nucleosynthesis (in "The Review of Particle Properties" 2004)
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• Deep Residual Learning for Image Recognition
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Machine learning and the physical sciences
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• The Pierre Auger Cosmic Ray Observatory
• ParticleNet: Jet Tagging via Particle Clouds
• Deep learning in color: towards automated quark/gluon jet discrimination
• Dynamic Graph CNN for Learning on Point Clouds
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Energy Flow Networks: Deep Sets for Particle Jets
• The Machine Learning Landscape of Top Taggers
• Jet Flavour Classification Using DeepJet
• The Belle II Core Software
• Deep-learned Top Tagging with a Lorentz Layer
• Graph Neural Networks in Particle Physics
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• JEDI-net: a jet identification algorithm based on interaction networks
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Spinodal amplification of density fluctuations in fluid-dynamical simulations of relativistic nuclear collisions
• Regressive and generative neural networks for scalar field theory
• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• The Full Event Interpretation -- An exclusive tagging algorithm for the Belle II experiment
• Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
• A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers
• MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• Lorentz Group Equivariant Neural Network for Particle Physics
• The Tracking Machine Learning challenge : Accuracy phase
• Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
• Casting a graph net to catch dark showers
• A machine learning study to identify spinodal clumping in high energy nuclear collisions
• Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
• Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering
• Graph Neural Networks for Particle Tracking and Reconstruction
• Deep learning stochastic processes with QCD phase transition
• A fast centrality-meter for heavy-ion collisions at the CBM experiment
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• The Boosted Higgs Jet Reconstruction via Graph Neural Network
• Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
• Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC
• Deep-Learning based Reconstruction of the Shower Maximum $X_{\max }$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory
• Playing Tag with ANN: Boosted Top Identification with Pattern Recognition
• Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
• Identification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks
• Track Seeding and Labelling with Embedded-space Graph Neural Networks
• Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors
• A tagger for strange jets based on tracking information using long short-term memory
• An equation-of-state-meter of QCD transition from deep learning
• Pileup mitigation at the Large Hadron Collider with Graph Neural Networks
• Identifying the nature of the QCD transition in heavy-ion collisions with deep learning
• Jet tagging in the Lund plane with graph networks

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• Machine Learning and LHC Event Generation
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• Exploring QCD matter in extreme conditions with Machine Learning
• Symmetries, Safety, and Self-Supervision
• OmniJet-$\alpha$: The first cross-task foundation model for particle physics
• Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes
• Learning Tree Structures from Leaves For Particle Decay Reconstruction
• Deep learning techniques for Imaging Air Cherenkov Telescopes