Machine learning-based event generator for electron-proton scattering

Alanazi, Y.; Ambrozewicz, P.; Battaglieri, M.; Hiller Blin, A.  N.; Kuchera, M.  P.; Li, Y.; Liu, T.; McClellan, R.  E.; Melnitchouk, W.; Pritchard, E.; Robertson, M.; Sato, N.; Strauss, R.; Velasco, L.

doi:10.1103/physrevd.106.096002

Title: Machine learning-based event generator for electron-proton scattering

Journal Article · 02 November 2022 · Physical Review. D.

DOI: https://doi.org/10.1103/physrevd.106.096002 · OSTI ID:1896514

^[1];

^[2]; Battaglieri, M. ^[3]; Hiller Blin, A. N. ^[4];

^[5]; Li, Y. ^[1];

^[6]; McClellan, R. E. ^[2];

^[2]; Pritchard, E. ^[5];

^[5]; Sato, N. ^[2]; Strauss, R. ^[5]; Velasco, L. ^[7]

Old Dominion Univ., Norfolk, VA (United States)
Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Istituto Nazionale di Fisica Nucleare (INFN), Genova (Italy)
Univ. of Tubingen (Germany); Univ. of Regensburg (Germany)
Davidson College, NC (United States)
Shandong Univ. (China)
Univ. of Dallas, Irving, TX (United States)

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.

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Research Organization:: Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP); German Research Foundation (DFG); National Science Foundation (NSF)

Grant/Contract Number:: AC05-06OR23177; 40824754; PHY2012865; LD2122

OSTI ID:: 1896514

Alternate ID(s):: OSTI ID: 1897171

Report Number(s):: JLAB-THY-20-3230; DOE/OR/23177-5013; arXiv:2008.03151; R&D Project: 2019-LDRD-13; 2020-LDRD-18; TRN: US2310767

Journal Information:: Physical Review. D., Vol. 106, Issue 9; ISSN 2470-0010

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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