Oct 3 – 6, 2022
Southern Methodist University
America/Chicago timezone

A Deep Learning Approach to Particle Identification for the AMS Electromagnetic Calorimeter

Oct 5, 2022, 1:30 PM
Southern Methodist University

Southern Methodist University


Raheem Hashmani (Middle East Technical University (TR))


The Alpha Magnetic Spectrometer (AMS-02) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. One of these, the Electromagnetic Calorimeter (ECAL), is used to measure the energy of cosmic-ray electrons and positrons and to differentiate these particles from cosmic-ray protons up to TeV energy.

We present a new deep learning approach for particle identification by taking as an input the energy deposition within all the calorimeter cells. By treating the cells as pixels in an image-like format, with effectively 2,592 features, we use various vision-based deep learning models as classifiers and compare their performances. Some of the models selected for training and evaluating range from simple convolutional neural networks (CNN) to more state-of-the-art residual neural networks (ResNet) and convolutional vision transformers (CvT).

The particle identification performance is evaluated using Monte Carlo electron and proton events from 100 GeV to 4 TeV. At 90% electron accuracy, for the entire energy range, the proton rejection power of our CvT model outperforms the CNN and ResNet models by more than a factor of 12 and 10, respectively. This shows promise for future use in the AMS-02 experiment and provides empirical evidence of newer architectures, such as transformers, outperforming CNNs for use in calorimeters.

Primary author

Raheem Hashmani (Middle East Technical University (TR))


Bilge Demirkoz (Middle East Technical University (TR)) Prof. Emre Akbaş (Middle East Technical University (TR)) Zhili Weng (Massachusetts Inst. of Technology (US))

Presentation materials