Publications
Publication Information
| Title | Machine learning-based event generator for electron-proton scattering |
| Authors | Yasir Alanazi, Pawel Ambrozewicz, Michelle Kuchera, Yaohang Li, Tianbo Liu, Randall McClellan, Wolodymyr Melnitchouk, Evan Pritchard, M. Robertson, Nobuo Sato, Ryan Strauss, L. Velasco |
| JLAB number | JLAB-THY-20-3230 |
| LANL number | arXiv:2008.03151 |
| Other number | DOE/OR/23177-5013 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | Yes |
| LDRD Numbers: | 2019-LDRD-13 2020-LDRD-18 |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for Physical Review D Volume 106 Page(s) 096002 Refereed | |
| Publication Abstract: | We present a novel new strategy using artificial intelligence (AI) to build the first AI-based Monte Carlo event generator (MCEG) capable of faithfully generating final state particle phase space in high-energy reactions. We show a blueprint for integrating machine learning strategy with calibrated detector simulations to build a vertex-level, AI-based MCEG, free of theoretical assumptions about femtometer scale physics. As the first steps towards this goal, we present a case study for inclusive electron-proton scattering using synthetic data from the PYTHIA MCEG for testing and validation purposes. Our quantitative results validate our proof of concept and demonstrate the predictive power of the trained models. The work suggests new venues for data preservation to enable future QCD studies of hadrons structure, and the developed technology can boost the science output of physics programs at facilities such as Jefferson Lab and the future Electron-Ion Collider. |
| Experiment Numbers: | other |
| Group: | THEORY CENTER |
| Document: | |
| DOI: | https://doi.org/10.1103/PhysRevD.106.096002 |
| Accepted Manuscript: | PhysRevD.106.096002.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |