TY - JOUR

T1 - Model independent analysis of coupled-channel scattering

T2 - A deep learning approach

AU - Sombillo, Denny Lane B.

AU - Ikeda, Yoichi

AU - Sato, Toru

AU - Hosaka, Atsushi

N1 - Funding Information:
This study was supported in part by MEXT as Program for Promoting Researches on the Supercomputer Fugaku (Simulation for basic science: from fundamental laws of particles to creation of nuclei). D. L. B. S. is supported in part by the DOST-SEI ASTHRDP postdoctoral research fellowship. Y. I. is partly supported by JSPS KAKENHI No. JP17K14287 (B) and No. 21K03555 (C). A. H. is supported in part by JSPS KAKENHI No. JP17K05441 (C) and Grants-in-Aid for Scientific Research on Innovative Areas, No. 18H05407 and No. 19H05104.
Publisher Copyright:
© 2021 authors. Published by the American Physical Society.

PY - 2021/8/1

Y1 - 2021/8/1

N2 - We develop a robust method to extract the pole configuration of a given partial-wave amplitude. In our approach, a deep neural network is constructed where the statistical errors of the experimental data are taken into account. The teaching dataset is constructed using a generic S-matrix parametrization, ensuring that all the poles produced are independent of each other. The inclusion of statistical error results into a noisy classification dataset which we should solve using the curriculum method. As an application, we use the elastic πN amplitude in the I(JP)=1/2(1/2-) sector where 106 amplitudes are produced by combining points in each error bar of the experimental data. We fed the amplitudes to the trained deep neural network and find that the enhancements in the πN amplitude are caused by one pole in each nearby unphysical sheet and at most two poles in the distant sheet. Finally, we show that the extracted pole configurations are independent of the way points in each error bar are drawn and combined, demonstrating the statistical robustness of our method.

AB - We develop a robust method to extract the pole configuration of a given partial-wave amplitude. In our approach, a deep neural network is constructed where the statistical errors of the experimental data are taken into account. The teaching dataset is constructed using a generic S-matrix parametrization, ensuring that all the poles produced are independent of each other. The inclusion of statistical error results into a noisy classification dataset which we should solve using the curriculum method. As an application, we use the elastic πN amplitude in the I(JP)=1/2(1/2-) sector where 106 amplitudes are produced by combining points in each error bar of the experimental data. We fed the amplitudes to the trained deep neural network and find that the enhancements in the πN amplitude are caused by one pole in each nearby unphysical sheet and at most two poles in the distant sheet. Finally, we show that the extracted pole configurations are independent of the way points in each error bar are drawn and combined, demonstrating the statistical robustness of our method.

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U2 - 10.1103/PhysRevD.104.036001

DO - 10.1103/PhysRevD.104.036001

M3 - Article

AN - SCOPUS:85112362601

VL - 104

JO - Physical Review D

JF - Physical Review D

SN - 2470-0010

IS - 3

M1 - 036001

ER -