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Machine learning-based event generator for electron-proton scattering

Year: 2020
Type: preprint
Abstract: We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof-of-concept to mitigate theory bias in inferring vertex-level event distributions needed to reconstruct physical observables.
DOI: 10.48550/arxiv.2008.03151
Authors: Alanazi, Y., Ambrozewicz, P., Battaglieri, M., Blin, A. N. Hiller, Kuchera, M. P., Li, Y., Liu, T., McClellan, R. E., Melnitchouk, W., Pritchard, E., Robertson, M., Sato, N., Strauss, R., Velasco, L.
Institutions: Old Dominion University, Thomas Jefferson National Accelerator Facility, Istituto Nazionale di Fisica Nucleare, Sezione di Genova, University of Tübingen, Davidson College, Shandong University, University of Dallas
Language: English
Open Access status: green
Awards: G1016821582, G1520178326, G1728142190, G2263355576, G2702141073, G2988778103, RUI: Machine Learning Approaches for Accelerating Scientific Discovery in Nuclear Physics, G6084215942, G7497762939, G7520763071
OpenAlex ID: W4287691973
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