第二十四届HAPOF论坛，Model-independent analysis of hadron-hadron scattering: a deep learning approach，Denny Sombillo 博士，2021年05月28日

Friday 28 May 2021, 15:00 → 16:30 Asia/Shanghai

报告人：Denny Sombillo 博士 (University of the Philippines Diliman, Osaka University）
报告时间：2021年05月28日 15：00 - 16：30

本届HAPOF采用zoom: 
https://zoom.us/j/92543231114

摘要：
Motivated by the amount of recent observations of new hadron phenomena, such as the XYZ states, we propose a deep learning method to analyze the origin of enhancement in the two-particle scattering cross-sections. The method identifies the pole configuration of a coupled channel scattering amplitudes, namely the number of nearby poles in each Riemann sheet associated with the structures in the scattering amplitude. I will discuss how a generic parametrized S-matrix generates the teaching dataset with controlled pole configurations, and how to optimize our deep neural network (DNN) model. In this study, we also include the limited energy resolution of experimental data, making the classification problem difficult to solve. To accelerate the training process with acceptable accuracy of the program, we employ the curriculum learning algorithm. For specific demonstration, we apply the present method to the elastic πN scattering amplitude with I(J^P )=1/2(1/2^-). We found that the enhancement structures in the πN amplitude are caused by one pole in each nearby sheet and two poles in the distance sheet. We also show that the output of the trained DNN during the inference stage is statistically robust. 

Refs.: 
arXiv:2105.04898 [hep-ph]; 
arXiv:2104.14182 [hep-ph]

报告人简介：
Dr. Denny Lane B. Sombillo
Assistant Professor, University of the Philippines Diliman
Specially Appointed Assistant Professor, Research Center for Nuclear Physics, Osaka University
2016 Ph.D. Physics, University of the Philippines Diliman
2011 M.S. Physics, University of the Philippines Diliman
2004 B.S. Physics, Philippine Normal University Manila, Philippines

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