Towards a data-driven model of hadronization using normalizing flows

被引：0
|
作者：
Bierlich, Christian [1 ]
Ilten, Phil [2 ]
Menzo, Tony [2 ,3 ,4 ]
Mrenna, Stephen [2 ,5 ]
Szewc, Manuel [2 ]
Wilkinson, Michael K. [2 ]
Youssef, Ahmed [2 ]
Zupan, Jure [2 ,3 ,4 ]
机构：
[1] Lund Univ, Dept Phys, Box 118, SE-22100 Lund, Sweden
[2] Univ Cincinnati, Dept Phys, Cincinnati, OH 45221 USA
[3] Univ Calif Berkeley, Berkeley Ctr Theoret Phys, Berkeley, CA 94720 USA
[4] Lawrence Berkeley Natl Lab, Theoret Phys Grp, Berkeley, CA 94720 USA
[5] Fermilab Natl Accelerator Lab, Sci Comp Div, Batavia, IL 60510 USA
来源：
SCIPOST PHYSICS | 2024年 / 17卷 / 02期
关键词：
QCD MODEL;
D O I：
10.21468/SciPostPhys.17.2.045
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.
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页数：26
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