Machine learning-based event generator for electron-proton scattering

Autor:	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.
Rok vydání:	2020
Předmět:	High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Theory
Druh dokumentu:	Working Paper
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:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.03151 Zobrazit plný text záznamu View this record from Arxiv