Quark Model Study of Doubly Heavy $\Xi$ and $\Omega$ Baryons via Deep Neural Network and Hybrid Optimization

TL;DR

This paper uses deep learning and hybrid optimization within the hypercentral quark model to predict masses and decay properties of doubly heavy baryons, aiding experimental searches.

Contribution

It introduces a novel combination of deep neural networks and particle swarm optimization to solve the Schrödinger equation for heavy baryons, enhancing prediction accuracy.

Findings

Predicted masses of ground and excited doubly heavy baryons.

Calculated semileptonic decay widths and branching ratios.

Provided results consistent with other theoretical models.

Abstract

In the present work we investigate the mass spectrum and semileptonic decays of double charm and bottom baryon states using the hypercentral quark model. We solve the six-dimensional Schr\"odinger equation via deep learning and particle swarm optimization techniques to improve the speed and accuracy. Then, we predict the masses of the ground and excited states of single and doubly heavy baryons. Working close to the zero recoil point, we also study the semileptonic decay widths and branching ratios of doubly heavy $\Xi$ and $\Omega$ baryons for the $b\rightarrow c$ transitions. A comparison between our results and the evaluations of other theoretical models is also presented. Our predictions of mass spectrum and decay widths provide valuable information for the experiment searching for undiscovered heavy baryon states.