A new machine learning approach for predicting the spectra of meson bound states

A. Yasser, T. A. Nahool, M. Anwar, C. Bowerman, G. A. Yahya
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引用次数: 1

Abstract

In this paper, we investigate the benefits of machine learning (ML) approaches in predicting the spectra of meson bound states. A linear model (LM) approach is used to predict the spectra of some heavy mesons. Our proposed method has been successfully reproduced in recent experiments, to validate known outcomes. Our results are compared favorably to those obtained using other techniques. This novel perspective opens up a new future in the use of ML in the field of particle physics.
预测介子束缚态谱的一种新的机器学习方法
在本文中,我们研究了机器学习(ML)方法在预测介子束缚态光谱方面的好处。用线性模型(LM)方法预测了一些重介子的谱。我们提出的方法已在最近的实验中成功地重现,以验证已知的结果。我们的结果与使用其他技术获得的结果相比是有利的。这种新颖的视角为机器学习在粒子物理领域的应用开辟了新的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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