Title: Meson mass and width: Deep learning approach

Abstract: It is fascinating to predict the mass and width of the ordinary and exotic mesons solely based on their quark content and quantum numbers. Such prediction goes beyond conventional methodologies traditionally employed in hadron physics for calculating or estimating these quantities. The relation between the quantum numbers and the properties of the mesons, such as the mass and width, is complicated in the world of particle physics. However, the deep neural network (DNN) as a subfield of machine learning techniques provides a solution to this problem. By analyzing large datasets, deep learning algorithms can automatically identify complex patterns among the particles' quantum numbers, and their mass and width, that would otherwise require complex calculations. In this study, we present two approaches using the DNNs to estimate the mass of some ordinary and exotic mesons. Also for the first time, the DNNs are trained to predict the width of ordinary and exotic mesons, whose widths have not been experimentally known. Our predictions obtained through the DNNs, will be useful for future experimental searches.