Unveiling the pole structure of S-matrix using deep learning
Unveiling the pole structure of S-matrix using deep learning
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DOI：
10.31349/suplrevmexfis.3.0308067
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Denny Lane B. Sombillo;Y. Ikeda;Toru Sato;A. Hosaka
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文献类型：
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作者：
Denny Lane B. Sombillo;Y. Ikeda;Toru Sato;A. Hosaka
粒子散射是揭示各种亚原子现象本质的强大工具。关键量是散射振幅，其解析结构携带了量子态的信息。在这项工作中，我们展示了使用深度学习方法提取非弹性散射极点配置的第一步尝试。在各种问题中，受最近新的强子现象的启发，我们开发了一种深度神经网络的课程学习方法来分析耦合通道散射问题。我们展示了该方法如何有效地提取与 πN 散射中的共振相关的极配置。
Particle scattering is a powerful tool to unveil the nature of various subatomic phenomena. The key quantity is the scattering amplitude whose analytic structure carries the information of the quantum states. In this work, we demonstrate our first step attempt to extract the pole configuration of inelastic scatterings using the deep learning method. Among various problems, motivated by the recent new hadron phenomena, we develop a curriculum learning method of deep neural network to analyze coupled channel scattering problems. We show how effectively the method works to extract the pole configuration associated with resonances in the πN scattering.