arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2003.10770hep-phhep-exnucl-exnucl-th
收藏

Classifying Pole of Amplitude Using Deep Neural Network

Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato, Atsushi Hosaka

英文摘要:

Most of exotic resonances observed in the past decade appear as peak structure near some threshold. These near-threshold phenomena can be interpreted as genuine resonant states or enhanced threshold cusps. Apparently, there is no straightforward way of distinguishing the two structures. In this work, we employ the strength of deep feed-forward neural network in classifying objects with almost similar features. We construct a neural network model with scattering amplitude as input and nature of pole causing the enhancement as output. The training data is generated by an S-matrix satisfying the unitarity and analyticity requirements. Using the separable potential model, we generate a validation data set to measure the network's predictive power. We find that our trained neural network model gives high accuracy when the cut-off parameter of the validation data is within $400$-$800\mbox{ MeV}$. As a final test, we use the Nijmegen partial wave and potential models for nucleon-nucleon scattering and show that the network gives the correct nature of pole.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑