Affiliation:
1. Department of Physics, Faculty of Science and Letters, Mimar Sinan Fine Arts University, Bomonti 34380, Istanbul, Turkey
Abstract
Recently, there have been significant developments in neural networks, which led to the frequent use of neural networks in the physics literature. This work focuses on predicting the masses of exotic hadrons, doubly charmed and bottomed baryons using neural networks trained on meson and baryon masses that are determined by experiments. The original dataset has been extended using the recently proposed artificial data augmentation methods. We have observed that the neural network’s predictive ability will increase with the use of augmented data. The results indicated that data augmentation techniques play an essential role in improving neural network predictions; moreover, neural networks can make reasonable predictions for exotic hadrons, doubly charmed, and doubly bottomed baryons. The results are also comparable to Gaussian Process and Constituent Quark Model.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Astronomy and Astrophysics,Nuclear and High Energy Physics,Atomic and Molecular Physics, and Optics