1. Machine learning-based event generator for electron-proton scattering
- Author
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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., and Velasco, L.
- Subjects
High Energy Physics - Phenomenology ,High Energy Physics - Experiment ,Nuclear Theory - 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., Comment: 20 pages, 8 figures, revised version, modified title, expanded author list
- Published
- 2020