JOURNAL ARTICLE

Predicting the masses of exotic hadrons with data augmentation using multilayer perceptron.

  • Published In: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2023, v. 38, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bahtiyar, Huseyin 3 of 3

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. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 2023/01, Vol. 38, Issue 1, p1
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:0217-751X
  • DOI:10.1142/S0217751X23500033
  • Accession Number:162360001
  • Copyright Statement:Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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