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The spatial-dependent-propagation (SDP) model with a nearby source works well to reproduce the coevolving features of both cosmic-ray (CR)-nuclei spectra and anisotropy. However, it is well known that the Sun is actually deviating from the galactic disk. This will lead to a dominating anisotropy in the direction perpendicular to the galactic disk, which is discrepant with current observations. Thus, it is necessary to further investigate the effect of the solar offset on anisotropy. In this work, to the best of our knowledge, this is the first time that the combined studies of the solar offset, nuclei spectra, and anisotropy have been performed based on the SDP model. As a result, to reproduce CR spectra and anisotropy, the thickness of the inner halo (Z I H) needs to increase linearly with the displacement of the Sun. We also know that the PeV anisotropy could be used to estimate the value of the diffusion coefficient, thus breaking the degeneracy between the diffusion coefficient and halo thickness. Therefore, it is a good approach to constrain the halo thickness. Moreover, the anisotropy in the PeV energy region, as a new probe, might also shed new light on constraining the solar offset. It is hoped that the anisotropy of the energies from ∼TeV to PeV can be finely measured with the LHAASO experiment, leading to a better understanding of the thick halo. [ABSTRACT FROM AUTHOR]

Galactic cosmic rays are mostly made up of energetic nuclei, with less than 1 % of electrons (and positrons). Precise measurement of the electron and positron component requires a very efficient method to reject the nuclei background, mainly protons. In this work, we develop an unsupervised machine learning method to identify electrons and positrons from cosmic ray protons for the Dark Matter Particle Explorer (DAMPE) experiment. Compared with the supervised learning method used in the DAMPE experiment, this unsupervised method relies solely on real data except for the background estimation process. As a result, it could effectively reduce the uncertainties from simulations. For three energy ranges of electrons and positrons, 80–128 GeV, 350–700 GeV, and 2–5 TeV, the residual background fractions in the electron sample are found to be about (0.45 ± 0.02)%, (0.52 ± 0.04)%, and (10.55 ± 1.80)%, and the background rejection power is about (6.21 ± 0.03) × 10 4 , (9.03 ± 0.05) × 10 4 , and (3.06 ± 0.32) × 10 4 , respectively. This method gives a higher background rejection power in all energy ranges than the traditional morphological parameterization method and reaches comparable background rejection performance compared with supervised machine learning methods. [ABSTRACT FROM AUTHOR]