Baryons from Mesons: A Machine Learning Perspective
This work addresses a specific problem in particle physics for researchers by providing a machine learning approach to predict hadron masses, but it is incremental as it builds on existing models like the constituent quark model.
The paper tackled predicting baryon masses from the meson spectrum using neural networks and Gaussian processes, achieving 90.3% and 96.6% accuracy, respectively, and extended predictions to exotic hadrons like pentaquarks.
Quantum chromodynamics (QCD) is the theory of the strong interaction. The fundamental particles of QCD, quarks and gluons, carry colour charge and form colourless bound states at low energies. The hadronic bound states of primary interest to us are the mesons and the baryons. From knowledge of the meson spectrum, we use neural networks and Gaussian processes to predict the masses of baryons with 90.3% and 96.6% accuracy, respectively. These results compare favourably to the constituent quark model. We as well predict the masses of pentaquarks and other exotic hadrons.