Towards a Deep Learning Model for Hadronization

Autor:	Ghosh, Aishik, Ju, Xiangyang, Nachman, Benjamin, Siodmok, Andrzej
Rok vydání:	2022
Předmět:	High Energy Physics - Phenomenology High Energy Physics - Experiment Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevD.106.096020
Popis:	Hadronization is a complex quantum process whereby quarks and gluons become hadrons. The widely-used models of hadronization in event generators are based on physically-inspired phenomenological models with many free parameters. We propose an alternative approach whereby neural networks are used instead. Deep generative models are highly flexible, differentiable, and compatible with Graphical Processing Unit (GPUs). We make the first step towards a data-driven machine learning-based hadronization model by replacing a compont of the hadronization model within the Herwig event generator (cluster model) with a Generative Adversarial Network (GAN). We show that a GAN is capable of reproducing the kinematic properties of cluster decays. Furthermore, we integrate this model into Herwig to generate entire events that can be compared with the output of the public Herwig simulator as well as with $e^+e^-$ data. Comment: 18 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.12660 Zobrazit plný text záznamu View this record from Arxiv