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Scrutinizing the evidence for dark matter in cosmic-ray antiprotons

Alessandro Cuoco1,2,*, Jan Heisig3,†, Lukas Klamt2,‡, Michael Korsmeier2,4,5,§, and Michael Krämer2,∥

  • 1Université Grenoble Alpes, USMB, CNRS, LAPTh, F-74940 Annecy, France
  • 2Institute for Theoretical Particle Physics and Cosmology, RWTH Aachen University, Sommerfeldstrasse 16, 52056 Aachen, Germany
  • 3Centre for Cosmology, Particle Physics and Phenomenology (CP3), Université catholique de Louvain, Chemin du Cyclotron 2, B-1348 Louvain-la-Neuve, Belgium
  • 4Dipartimento di Fisica, Università di Torino, Via P. Giuria 1, 10125 Torino, Italy
  • 5Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, 10125 Torino, Italy

  • *cuoco@physik.rwth-aachen.de
  • †jan.heisig@uclouvain.be
  • ‡klamt@physik.rwth-aachen.de
  • §korsmeier@physik.rwth-aachen.de
  • ∥mkraemer@physik.rwth-aachen.de
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Phys. Rev. D 99, 103014 – Published 23 May, 2019

DOI: https://doi.org/10.1103/PhysRevD.99.103014

Abstract

Global fits of primary and secondary cosmic-ray (CR) fluxes measured by AMS-02 have great potential to study CR propagation models and search for exotic sources of antimatter such as annihilating dark matter (DM). Previous studies of AMS-02 antiprotons revealed a possible hint for a DM signal which, however, could be affected by systematic uncertainties. To test the robustness of such a DM signal, in this work we systematically study two important sources of uncertainties: the antiproton production cross sections needed to calculate the source spectra of secondary antiprotons and the potential correlations in the experimental data, so far not provided by the AMS-02 Collaboration. To investigate the impact of cross-section uncertainties we perform global fits of CR spectra including a covariance matrix determined from nuclear cross-section measurements. As an alternative approach, we perform a joint fit to both the CR and cross-section data. The two methods agree and show that cross-section uncertainties have a small effect on the CR fits and on the significance of a potential DM signal, which we find to be at the level of 3σ. Correlations in the data can have a much larger impact. To illustrate this effect, we determine possible benchmark models for the correlations in a data-driven method. The inclusion of correlations strongly improves the constraints on the propagation model and, furthermore, enhances the significance of the DM signal up to above 5σ. Our analysis demonstrates the importance of providing the covariance of the experimental data, which is needed to fully exploit their potential.

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Phys. Rev. D 99, 103014– Published 23 May, 2019
  • Received 14 March 2019
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