Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron

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 is focused 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 data set has been extended using the recently proposed artificial data augmentation methods. We have observed that the neural network's predictive ability increases 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.

arXiv comment8 pages, 4 figures

References in corpus (30)

• Observation of a narrow charmonium-like state in exclusive B+ -> K+ pi+pi- J/psi decays
• Observation of a New Narrow Charmonium State in Exclusive B+-->K+ pi+pi- J/psi Decays
• Observation of $J/\psi p$ resonances consistent with pentaquark states in ${\Lambda }_b^0\to J/\psi K^{-}p$ decays
• Observation of a charged charmoniumlike structure in e+e- to pi+pi-J/psi at \sqrt{s}=4.26 GeV
• Observation of a narrow pentaquark state, $P_c(4312{)}^{+}$, and of two-peak structure of the $P_c(4450{)}^{+}$
• The LHCb Trigger and its Performance in 2011
• Observation of two charged bottomonium-like resonances in Y(5S) decays
• Heavy Baryons in a Quark Model
• Determination of the X(3872) meson quantum numbers
• Deep Learning and its Application to LHC Physics
• Charmed bottom baryon spectroscopy from lattice QCD
• Analysis of the Quantum Numbers $J^{PC}$ of the X(3872) Particle
• Evidence for the decay X(3872) --> J/psi omega
• Bounds on the width, mass difference and other properties of X(3872) --> pi+pi-J/psi decays
• Charmed Baryon Physics Circa 2021
• Doubly heavy baryons in QCD sum rules
• Nuclear mass systematics using neural networks
• Taming nuclear complexity with a committee of multilayer neural networks
• Machine learning action parameters in lattice quantum chromodynamics
• Background rejection in NEXT using deep neural networks
• Charmed baryon spectrum from lattice QCD near the physical point
• Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron
• Machine Learning Estimators for Lattice QCD Observables
• A Review on Machine Learning for Neutrino Experiments
• A Study of Excited ${\Omega }_b^{-}$ States in Hypercentral Constituent Quark Model via Artificial Neural Network
• Application of multilayer perceptron with data augmentation in nuclear physics
• Classifying near-threshold enhancement using deep neural network
• Using Machine Learning to Improve Neutron Identification in Water Cherenkov Detectors
• General recipe to form input space for deep learning analysis of HEP scattering processes
• Nuclear binding energy predictions based on BP neural network