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Deciphering the Local Interstellar Spectra of Secondary Nuclei with the Galprop/Helmod Framework and a Hint for Primary Lithium in Cosmic Rays.

M J BoschiniS Della TorreM GervasiD GrandiGuðlaugur JóhannessonG La VaccaN MasiIgor V MoskalenkoS PensottiTroy A PorterL QuadraniP G RancoitaD RozzaM Tacconi
Published in: The Astrophysical journal (2020)
Local interstellar spectra (LIS) of secondary cosmic-ray (CR) nuclei, lithium, beryllium, boron, and partially secondary nitrogen, are derived in the rigidity range from 10 MV to ~200 TV using the most recent experimental results combined with state-of-the-art models for CR propagation in the Galaxy and in the heliosphere. The lithium spectrum appears somewhat flatter at high energies compared to other secondary species, which may imply a primary lithium component. Two propagation packages, GALPROP and HelMod, are combined to provide a single framework that is run to reproduce direct measurements of CR species at different modulation levels, and at both polarities of the solar magnetic field. An iterative maximum-likelihood method is developed that uses GALPROP-predicted LIS as input to HelMod, which provides the modulated spectra for specific time periods of the selected experiments for the model-data comparison. The proposed LIS accommodates the low-energy interstellar spectra measured by Voyager 1, the High Energy Astrophysics Observatory-3 (HEAO-3), and the Cosmic Ray Isotope Spectrometer on board of the Advanced Composition Explorer (ACE/CRIS), as well as the high-energy observations by the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA), Alpha Magnetic Spectrometer-02 (AMS-02), and earlier experiments that are made deep in the heliosphere. The interstellar and heliospheric propagation parameters derived in this study are consistent with our earlier results for propagation of CR protons, helium, carbon, oxygen, antiprotons, and electrons.
Keyphrases
  • density functional theory
  • solid state
  • high resolution
  • magnetic resonance imaging
  • molecular dynamics
  • big data
  • machine learning
  • simultaneous determination
  • amino acid