Deep Learning as a Parton Shower
arXiv:1807.03685 · doi:10.1007/JHEP12(2018)021
Abstract
We make the connection between certain deep learning architectures and the renormalisation group explicit in the context of QCD by using a deep learning network to construct a toy parton shower model. The model aims to describe proton-proton collisions at the Large Hadron Collider. A convolutional autoencoder learns a set of kernels that efficiently encode the behaviour of fully showered QCD collision events. The network is structured recursively so as to ensure self-similarity, and the number of trained network parameters is low. Randomness is introduced via a novel custom masking layer, which also preserves existing parton splittings by using layer-skipping connections. By applying a shower merging procedure, the network can be evaluated on unshowered events produced by a matrix element calculation. The trained network behaves as a parton shower that qualitatively reproduces jet-based observables.
arXiv comment26 pages, 13 figures
References in corpus (25)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- The anti-k_t jet clustering algorithm
- FastJet user manual
- An overview of gradient descent optimization algorithms
- Parton distributions for the LHC Run II
- Event generation with SHERPA 1.1
- Herwig++ Physics and Manual
- A parton shower algorithm based on Catani-Seymour dipole factorisation
- Successive Combination Jet Algorithm For Hadron Collisions
- Comix, a new matrix element generator
- QCD matrix elements and truncated showers
- Rivet user manual
- AMEGIC++ 1.0, A Matrix Element Generator In C++
- NLO matrix elements and truncated showers
- Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
- Deep learning in color: towards automated quark/gluon jet discrimination
- Jet-Images -- Deep Learning Edition
- New formalism for QCD parton showers
- Finding top quarks with shower deconstruction
- A modified CKKW matrix element merging approach to angular-ordered parton showers
- Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
- Deep learning and the renormalization group
- Computation and Universality: Class IV versus Class III Cellular Automata
- Development Strategies for Pythia version 7
- Criticality & Deep Learning II: Momentum Renormalisation Group
Cited by in corpus (36)
- Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
- Anomaly Detection with Density Estimation
- Searching for New Physics with Deep Autoencoders
- How to GAN LHC Events
- Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
- Adversarially-trained autoencoders for robust unsupervised new physics searches
- Machine Learning and LHC Event Generation
- Score-based Generative Models for Calorimeter Shower Simulation
- Invertible Networks or Partons to Detector and Back Again
- Neural Networks for Full Phase-space Reweighting and Parameter Tuning
- How to GAN away Detector Effects
- Understanding Event-Generation Networks via Uncertainties
- Event Generators for High-Energy Physics Experiments
- GANplifying Event Samples
- Generative Networks for Precision Enthusiasts
- A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
- A robust anomaly finder based on autoencoders
- Fast Point Cloud Generation with Diffusion Models in High Energy Physics
- Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
- A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
- Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
- Calomplification -- The Power of Generative Calorimeter Models
- Towards a Deep Learning Model for Hadronization
- Lorentz group equivariant autoencoders
- Explainable machine learning of the underlying physics of high-energy particle collisions
- Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning
- How to GAN Event Subtraction
- Learning Physics at Future Colliders with Machine
- OASIS: Optimal Analysis-Specific Importance Sampling for event generation
- How to GAN Event Unweighting
- Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning
- Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
- The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC
- Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
- Latent Space Refinement for Deep Generative Models
- Data-driven detector signal characterization with constrained bottleneck autoencoders