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PoS(ICRC2017)163

Capability of electron identification for the CALET measurement.

L. Pacini, Y. Akaike, on behalf of the CALET Collaboration

in 35th International Cosmic Ray Conference

Contribution: pdf

Abstract

The CALorimetric Electron Telescope, CALET, was installed on the International Space Station in August 2015 and it has been collecting data of high energy cosmic rays since October 2015. The primary purpose of CALET mission is obtaining high-precision direct measurements of the electron+positron spectrum up to the multi-TeV energy region, which can provide unique information on the presence of nearby astrophysical sources and possible signals from dark matter. Other important objectives are the observation of nuclei spectra from proton to Fe up to several hundred TeV energies and the detection of gamma-ray emissions up to ∼10 TeV with high-accuracy energy measurement.
The CALET instrument consists of a charge detection device composed of two layers of plastic scintillators, a finely-segmented sampling calorimeter (IMC) with imaging capabilities made of scintillating fibers (with total depth of 3 radiation lengths) and a homogeneous calorimeter (TASC) with PWO scintillating logs (27 radiation lengths).
Since protons are the largest background source for the electron measurements and the ratio of protons to electrons increases at higher energies, a proton rejection power of $10^5$ is required in the TeV region. Owing to the thick and well-segmented calorimeter, CALET has capabilities to absorb most of shower energy for electrons in TeV region and to identify electrons from protons by the difference of shapes between electromagnetic showers and hadronic showers. The present work contains a comparative study, with different Monte Carlo simulation codes, of the proton rejection power obtained with optimized selection cuts. In order to maximize the rejection power we have also confirmed the consistency between flight data and Monte Carlo simulations for the variables obtained by the reconstruction of shower images and we have applied multivariate analysis (MVA) with boosted decision tree algorithms. The resulting capability of electron identification with CALET will be presented.