标题： 使用神经网络对活性目标漂移室中霍伊尔态衰变分支的分类 显示英文标题

标题： Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network

摘要： 利用 Python 的 Keras 深度学习库开发了一种多类卷积神经网络（CNN）模型，用于基于图像的分类$^{12}$C 木星态衰变分支，这些衰变分支通过 Saha 主动靶时间投影室（SAT-TPC，目前处于开发阶段）记录的跟踪信息获得。 考虑了由 30 MeV $\alpha$粒子束在 SAT-TPC 中产生的核事件进行模型的训练和验证，SAT-TPC 填充有 Ar + CO$_2$（90:10）气体混合物，在大气压条件下。 通过 Monte-Carlo 模拟生成了 $\alpha$粒子与 $^{40}$Ar、$^{12}$C、$^{16}$O 核相互作用中的弹性散射和木星态序列及直接衰变事件。 通过 Geant4 模拟了由气体介质初级电离产生的散射和衰变产物的三维轨迹。 通过将主要分布在束流平面上的初级轨迹与通过 Magboltz 获得的电子扩散相结合，生成最终的跟踪信息。 讨论了所提出的模型针对 SAT-TPC 不同读出分割方案的分类性能。 显示英文摘要

摘要： A multi-class convolutional neural network (CNN) model has been developed using Keras deep learning library in Python for image-based classification of $^{12}$C Hoyle state decay branches from tracking information, recorded by Saha Active Target Time Projection Chamber, SAT-TPC (currently under development). The nuclear events, produced by the 30 MeV $\alpha$-particle beam in the SAT-TPC, filled with Ar + CO$_2$ (90:10) gas mixture at atmospheric pressure, have been considered for training and validation of the models. The elastic scattering and Hoyle state sequential and direct decay events in the interaction of $\alpha$-particle with $^{40}$Ar, $^{12}$C, $^{16}$O nuclei have been generated through Monte-Carlo simulation. The three-dimensional tracks, produced by the scattering and decay products through primary ionization of gaseous medium, have been simulated with Geant4. The primary tracks, distributed on the beam-plane, have been convoluted with electron diffusion, obtained with Magboltz, to produce the final tracking information. The classification performance of the proposed model for different readout segmentation schemes of the SAT-TPC has been discussed.