Masayoshi Kozai

Abstract Gamma rays from HESS J1849−000, a middle-aged TeV pulsar wind nebula (PWN), are observed by the Tibet air shower array and the muon detector array. The detection significance of gamma rays reaches 4.0σ and 4.4σ levels above 25 TeV and 100 TeV, respectively, in units of the Gaussian standard deviation σ. The energy spectrum measured between 40 TeV < E < 320 TeV for the first time is described with a simple power-law function of ${dN}/{dE}={(2.86\pm 1.44)\times {10}^{-16}(E/40\,\mathrm{TeV})}^{-2.24\pm 0.41}\,{\mathrm{TeV } }^{-1}\,{\mathrm{cm } }^{-2}\,{ { \rm{s } } }^{-1}$. The gamma-ray energy spectrum from the sub-TeV (E < 1 TeV) to sub-PeV (100 TeV < E < 1 PeV) ranges, including the results of previous studies, can be modeled with the leptonic scenario, i.e., inverse Compton scattering by high-energy electrons accelerated by the PWN of PSR J1849−0001. On the other hand, the gamma-ray energy spectrum can also be modeled with the hadronic scenario in which gamma rays are generated from the decay of neutral pions produced by collisions between accelerated cosmic-ray protons and the ambient molecular cloud found in the gamma-ray-emitting region. The cutoff energy of cosmic-ray protons E_p,cut is estimated as ${\mathrm{log } }_{10}({E}_{ { \rm{p } },\mathrm{cut } }/\mathrm{TeV})={3.73}_{-0.66}^{+2.98}$, suggesting that protons are accelerated up to the PeV energy range. Our study thus proposes that HESS J1849−000 should be further investigated as a new candidate as a Galactic PeV cosmic-ray accelerator, or “PeVatron.”

The General Antiparticle Spectrometer (GAPS) is a balloon-borne experiment that aims to measure low-energy cosmicray antiparticles. GAPS has developed a new antiparticle identification technique based on exotic atom formation caused by incident particles, which is achieved by ten layers of Si(Li) detector tracker in GAPS. The conventional analysis uses the physical quantities of the reconstructed incident and secondary particles. In parallel with this, we have developed a complementary approach based on deep neural networks. This paper presents a new convolutional neural network (CNN) technique. A three-dimensional CNN takes energy depositions as three-dimensional inputs and learns to identify their positional/energy correlations. The combination of the physical quantities and the CNN technique is also investigated. The findings show that the new technique outperforms existing machine learning-based methods in particle identification.