利用人工神经网络求解查莫能谱的薛定谔波方程

IF 1.5 4区 物理与天体物理 Q3 PHYSICS, PARTICLES & FIELDS
Tariq Mahmood, Jumanah Ahmed Darwish, Talab Hussain, Maqsood Ahmed, Rehan Ahmad Khan Sherwani
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引用次数: 0

摘要

在这项研究中,我们利用人工神经网络(ANN)中的有效电势求解了薛定谔波方程,得到了不同粲态(包括 、 、 和 )的质谱。人工神经网络方法提供了一种高效、通用和连续的求解逼近策略,从而消除了在质谱分析中跳过任何感兴趣区域的可能性。方差网络的结果与已报道的数值和理论方法以及实验结果非常一致,这表明了方差网络方法的可靠性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving Schrödinger Wave Equation for the Charmonium Spectrum Using Artificial Neural Networks
In this study, we solved the Schrödinger wave equation by using effective potential in an artificial neural network (ANN) for the mass spectrum of different charmonium states, including , , , and . The ANN approach provides an efficient, more general, and continuous solution-approximating strategy, thus eliminating the possibility of skipping any region of interest in mass spectroscopy. The close consistency of ANN results with the already-reported results from numerical and theoretical approaches and experimental ones shows the reliability and accuracy of the ANN approach.
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来源期刊
Advances in High Energy Physics
Advances in High Energy Physics PHYSICS, PARTICLES & FIELDS-
CiteScore
3.40
自引率
5.90%
发文量
55
审稿时长
6-12 weeks
期刊介绍: Advances in High Energy Physics publishes the results of theoretical and experimental research on the nature of, and interaction between, energy and matter. Considering both original research and focussed review articles, the journal welcomes submissions from small research groups and large consortia alike.
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