4.7 Article

New minimal, median, and maximal propagation models for dark matter searches with Galactic cosmic rays

期刊

PHYSICAL REVIEW D
卷 104, 期 8, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.104.083005

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资金

  1. Universite de Savoie
  2. national CNRS/(Institut national des sciences de l'Univers)
  3. PNHE (Programme National Hautes Energies)
  4. Programme National de Cosmologie et de Galaxies (PNCG) programs
  5. INP
  6. IN2P3
  7. CEA
  8. CNES
  9. Villum Fonden [18994]
  10. ANR [ANR-18-CE310006]
  11. Origines, Constituants et EVolution de l'Univers (OCEVU) Labex [ANR-11-LABX-0060]
  12. European Union [860881]
  13. CNRS

向作者/读者索取更多资源

Galactic charged cosmic rays are powerful tools for investigating dark matter, but the constraints are subject to uncertainties related to Galactic transport models. Updating the MIN-MED-MAX benchmark transport parameters has reduced uncertainties and provided fitting formulas for background flux predictions. Full details of model parameters and covariance matrices are also provided for more refined analyses.
Galactic charged cosmic rays (notably electrons, positrons, antiprotons, and light antinuclei) are powerful probes of dark matter annihilation or decay, in particular, for candidates heavier than a few MeV or tiny evaporating primordial black holes. Recent measurements by PAMELA, AMS-02, or Voyager on positrons and antiprotons already translate into constraints on several models over a large mass range. However, these constraints depend on Galactic transport models, in particular, the diffusive halo size, subject to theoretical and statistical uncertainties. We update the so-called MIN-MED-MAX benchmark transport parameters that yield generic minimal, median, and maximal dark-matter-induced fluxes; this reduces the uncertainties on fluxes by a factor of about 2 for positrons and 6 for antiprotons, with respect to their former version. We also provide handy fitting formulas for the associated predicted secondary antiproton and positron background fluxes. Finally, for more refined analyses, we provide the full details of the model parameters and covariance matrices of uncertainties.

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