Alanazi, Y. and Ambrozewicz, P. and Battaglieri, M. and Blin, A. N. Hiller and Kuchera, M. P. and Li, Y. and Liu, T. and McClellan, R. E. and Melnitchouk, W. and Pritchard, E. and Robertson, M. and Sato, N. and Strauss, R. and Velasco, L. (2022) Machine learning-based event generator for electron-proton scattering. PHYSICAL REVIEW D, 106 (9): 096002. ISSN 2470-0010, 2470-0029
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ; |
| Subjects: | 500 Science > 530 Physics |
| Divisions: | Physics > Institute of Theroretical Physics |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 01 Feb 2024 15:16 |
| Last Modified: | 01 Feb 2024 15:16 |
| URI: | https://pred.uni-regensburg.de/id/eprint/58344 |
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