Measuring QCD Splittings with Invertible Networks
arXiv:2012.09873 · doi:10.21468/SciPostPhys.10.6.126
Abstract
QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level jet observables. Starting with an toy example we study the effect of the full shower, hadronization, and detector effects in detail.
25 pages, 11 figures
References in corpus (47)
- FastJet user manual
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Normalizing Flows: An Introduction and Review of Current Methods
- Better Jet Clustering Algorithms
- Event Generation with Sherpa 2.2
- The Dipole Formalism for the Calculation of QCD Jet Cross Sections at Next-to-Leading Order
- Towards an understanding of jet substructure
- ParticleNet: Jet Tagging via Particle Clouds
- Energy Correlation Functions for Jet Substructure
- Anomaly Detection with Density Estimation
- Deep learning in color: towards automated quark/gluon jet discrimination
- Jet-Images -- Deep Learning Edition
- Deep-learning Top Taggers or The End of QCD?
- Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
- Energy Flow Networks: Deep Sets for Particle Jets
- The Machine Learning Landscape of Top Taggers
- Event Generation with Normalizing Flows
- Deep-learned Top Tagging with a Lorentz Layer
- Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
- Event shapes in e+e- annihilation and deep inelastic scattering
- Parton showers beyond leading logarithmic accuracy
- Constraining Effective Field Theories with Machine Learning
- Pulling Out All the Tops with Computer Vision and Deep Learning
- The Lund Jet Plane
- QCD-Aware Recursive Neural Networks for Jet Physics
- Logarithmic accuracy of parton showers: a fixed-order study
- i-flow: High-dimensional Integration and Sampling with Normalizing Flows
- Multivariate discrimination and the Higgs + W/Z search
- Exploring phase space with Neural Importance Sampling
- Invertible Networks or Partons to Detector and Back Again
- Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
- Implementing NLO DGLAP evolution in Parton Showers
- Precision Jet Substructure from Boosted Event Shapes
- Leading-color fully differential two-loop soft corrections to QCD dipole showers
- Tests of Quantum Chromo Dynamics at e^+e^- Colliders
- A framework for second-order parton showers
- Antenna Showers with One-Loop Matrix Elements
- Quark-Gluon Tagging: Machine Learning vs Detector
- Deep-Learning Jets with Uncertainties and More
- Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
- A Simultaneous Measurement of the QCD Colour Factors and the Strong Coupling
- Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering
- Per-Object Systematics using Deep-Learned Calibration
- Improved Neural Network Monte Carlo Simulation
- A Measurement of the QCD Colour Factors using Event Shape Distributions at sqrt(s)=14 GeV to 189 GeV
- Particle multiplicity of unbiased gluon jets from e+e- three-jet events
- Jet physics in e+e- annihilation from 14 to 209 GeV
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- Machine Learning and LHC Event Generation
- CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
- Understanding Event-Generation Networks via Uncertainties
- Event Generators for High-Energy Physics Experiments
- Generative Networks for Precision Enthusiasts
- MadNIS -- Neural Multi-Channel Importance Sampling
- How to Understand Limitations of Generative Networks
- Two Invertible Networks for the Matrix Element Method
- The MadNIS Reloaded
- What's Anomalous in LHC Jets?
- Returning CP-Observables to The Frames They Belong
- CaloFlow for CaloChallenge Dataset 1
- CaloDREAM -- Detector Response Emulation via Attentive flow Matching
- Precision-Machine Learning for the Matrix Element Method
- Normalizing Flows for High-Dimensional Detector Simulations
- Calorimeter shower superresolution
- Differentiable MadNIS-Lite
- Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN
- Systematic Quark/Gluon Identification with Ratios of Likelihoods
- Anomaly detection with flow-based fast calorimeter simulators
- Accurate Surrogate Amplitudes with Calibrated Uncertainties
- Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction
- TopicFlow: Disentangling quark and gluon jets with normalizing flows
- Unifying Simulation and Inference with Normalizing Flows
- Reconstructing axion-like particles from beam dumps with simulation-based inference
- Classifier Surrogates: Sharing AI-based Searches with the World
- BitHEP -- The Limits of Low-Precision ML in HEP
- Extrapolating Jet Radiation with Autoregressive Transformers
- Event Generation and Density Estimation with Surjective Normalizing Flows
- Implicit Quantile Networks For Emulation in Jet Physics