Abstract. CALET (Calorimetric Electron Telescope), installed on the ISS in August 2015,directly measures the electron+positron cosmic rays flux up to 20 TeV. With its proton rejection capability of 1 : 10 5 and an aperture of 1200 cm 2 ·sr, it will provide good statistics even well above one TeV, while also featuring an energy resolution of 2%, which allows it to detect fine structures in the spectrum. Such structures may originate from Dark Matter annihilation or decay, making indirect Dark Matter search one of CALET's main science objectives among others such as identification of signatures from nearby supernova remnants, study of the heavy nuclei spectra and gamma astronomy. The latest results from AMS-02 on positron fraction and total electron+positron flux can be fitted with a parametrization including a single pulsar as an extra power law source with exponential cut-off, which emits an equal amount of electrons and positrons. This single pulsar scenario for the positron excess is extrapolated into the TeV region and the expected CALET data for this case are simulated. Based on this prediction for CALET data, the sensitivity of CALET to Dark Matter annihilation in the galactic halo has been calculated. It is shown that CALET could significantly improve the limits compared to current data, especially for those Dark Matter candidates that feature a large fraction of annihilation directly into e + + e − , such as the LKP (Lightest Kaluza-Klein particle).

Abstract. The ISS-based CALET (CALorimetric Electron Telescope) detector can play an important role in indirect search for Dark Matter (DM), measuring the electron+positron flux in the TeV region for the first time directly. With its fine energy resolution of approximately 2% and good proton rejection ratio (1 : 10 5 ) it has the potential to search for fine structures in the Cosmic Ray (CR) electron spectrum. In this context we discuss the ability of CALET to discern between signals originating from astrophysical sources and DM decay. We fit a parametrization of the local interstellar electron and positron spectra to current measurements, with either a pulsar or 3-body decay of fermionic DM as the extra source causing the positron excess. The expected CALET data for scenarios in which DM decay explains the excess are calculated and analyzed. The signal from this particular 3-body DM decay which can explain the recent measurements from the AMS−02 experiment is shown to be distinguishable from a single pulsar source causing the positron excess by 5 years of observation with CALET, based on the shape of the spectrum. We also study the constraints from diffuse γ-ray data on this DM-only explanation of the positron excess and show that especially for the possibly remaining parameter space a clearly identifiable signature in the CR electron spectrum exists.

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With limited statistics and spatial resolution of current detectors, accurately localizing and separating -ray point sources from the dominating interstellar emission in the GeV energy range is challenging. Motivated by the challenges of the traditional methods used for the -ray source detection, here we demonstrate the application of deep learning based algorithms to automatically detect and classify point sources, which can be applied directly to the binned Fermi-LAT data and potentially be generalized to other wavelengths. For the point source detection task, we use popular deep neural network structure, U-NET and, together with image segmentation, for precise localization of sources, various clustering algorithms were tested on the segmented images. The training samples are based on the source properties of AGNs and PSRs from the latest Fermi-LAT source catalog in addition to the background interstellar emission. Results obtained using these algorithms are shown to be comparable with the traditional methods and checked the robustness by using different interstellar emission models. We also publish our training data-sets and analysis scripts and, invite the community to a challenge -find the best possible method for localizing and classifying -ray sources. Finally, we present a more complex but robust training data generation exploiting full detector potential, which will be used in our follow-up work.