The Alpha Magnetic Spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The Transition Radiation Detector and Electromagnetic Calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. The positron flux measured in space by AMS falls with a power law which unexpectedly softens above 25 GeV and then hardens above 280 GeV. Several theoretical models try to explain these phenomena, and a purer measurement of positrons at higher energies is needed to help test them. The currently used methods to reject the proton background at high energies involve extrapolating shower features from the ECAL to use as inputs for boosted decision tree and likelihood classifiers. We present a new approach for particle identification with the AMS ECAL using deep learning (DL). By taking the energy deposition within all the ECAL cells as an input and treating them as pixels in an image-like format, we train an MLP, a CNN, and multiple ResNets and Convolutional vision Transformers (CvTs) as shower classifiers. Proton rejection performance is evaluated using Monte Carlo (MC) events and ISS data separately. For MC, using events with a reconstructed energy between 0.2 - 2 TeV, at 90% electron accuracy, the proton rejection power of our CvT model is more than 5 times that of the other DL models. Similarly, for ISS data with a reconstructed energy between 50 - 70 GeV, the proton rejection power of our CvT model is more than 2.5 times that of the other DL models.

翻译：阿尔法磁谱仪（AMS）是搭载于国际空间站的高精度粒子探测器，包含六个不同子探测器。过渡辐射探测器与电磁量能器（ECAL）用于将电子/正电子从丰沛的宇宙射线质子本底中分离。AMS在太空中测量的正电子通量服从幂律分布，在25 GeV以上意外软化，随后在280 GeV以上再次硬化。多种理论模型试图解释这些现象，需更高能量下更纯净的正电子测量来验证这些模型。当前高能量质子本底抑制方法通过外推ECAL簇射特征，将其作为提升决策树和似然分类器的输入。我们提出一种基于深度学习（DL）的新型AMS ECAL粒子鉴别方法：将所有ECAL单元的沉积能量作为输入，并将其视为类图像格式的像素，训练MLP、CNN、多种ResNet及卷积视觉Transformer（CvT）作为簇射分类器。分别使用蒙特卡洛（MC）事件和国际空间站（ISS）数据评估质子抑制性能。对于MC事件，在重建能量0.2-2 TeV范围内、电子鉴别效率90%条件下，CvT模型的质子抑制能力是其他DL模型的5倍以上；对于ISS数据，在重建能量50-70 GeV范围内，CvT模型的质子抑制能力是其他DL模型的2.5倍以上。