Unfolding with Generative Adversarial Networks

Abstract

Correcting measured detector-level distributions to particle-level is essential to make data usable outside the experimental collaborations. The term unfolding is used to describe this procedure. A new method of unfolding data using a modified Generative Adversarial Network (MSGAN) is presented here. Applied to various distributions with widely different shapes, it performs roughly at par with currently used methods. This is a proof-of-principle demonstration of a state-of-the-art machine learning method that can be used to model detector effects well.

11 pages, 10 figures, prepared for submission to JHEP

References in corpus (14)

• Adam: A Method for Stochastic Optimization
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• A Brief Introduction to PYTHIA 8.1
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Improved Techniques for Training GANs
• Rivet user manual
• Energy Correlation Functions for Jet Substructure
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Systematics of quark/gluon tagging
• Fast and accurate simulation of particle detectors using generative adversarial networks
• Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
• Machine learning approach to inverse problem and unfolding procedure

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