Unfolding with Generative Adversarial Networks

2018/06/01 by Kaustuv Datta, K. Datta, D. Kar +6 · 10 citations
Computer Science · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an) #hep-ex #hep-ph #physics.data-an

paper · pdf · doi:10.48550/arxiv.1806.00433

11 pages, 10 figures, prepared for submission to JHEP

openalex publication_date 2018/06/01 · arxiv created 2018/08/04 · arxiv updated 2018/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Improved Techniques for Training GANs

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• Preserving new physics while simultaneously unfolding all observables
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• Reconstructing the kinematics of deep inelastic scattering with deep learning

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