Machine learning-based event generator for electron-proton scattering
| Autor: | Alanazi, Y., Ambrozewicz, P., Battaglieri, M., Hiller Blin, Astrid N., Kuchera, M. P., Li, Y., Liu, T., McClellan, R. E., Melnitchouk, W., Pritchard, E., Robertson, M., Sato, N., Strauss, R., Velasco, L. |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Physical Review |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|