A Comparison of Deep Learning Models for Proton Background Rejection
  with the AMS Electromagnetic Calorimeter
Raheem Karim Hashmani Emre Akbaş Melahat Bilge Demirköz
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.