Mar 27 – 29, 2019
UCLA Faculty Center
US/Pacific timezone

Scrutinizing the evidence for dark matter in cosmic-ray antiprotons

Mar 27, 2019, 11:45 AM
20m
Hacienda Room (UCLA Faculty Center)

Hacienda Room

UCLA Faculty Center

UCLA Faculty Center 480 Charles Young Dr. East Los Angeles, CA USA
oral

Speaker

Michael Korsmeier (University of Turin)

Description

During the last decade, the space-based experiment AMS-02 has drastically reduced the measurement uncertainty of primary and secondary cosmic-ray fluxes. Therefore, global fits of these fluxes provide great potential to study comic-ray propagation models and search for exotic sources of antimatter such as annihilating dark matter. Previous studies of AMS-02 antiprotons revealed a possible hint for a dark matter signal which, intriguing, would be in agreement with the dark matter interpretation of the gamma-ray excess at the Galactic center. On the other hand, systematic uncertainties in the theoretical description of cosmic rays become gradually more relevant as the data uncertainties are reduced. I will discuss two important sources of uncertainties in order to test the robustness of the putative dark matter signal: the antiproton production cross-sections needed to calculate the source spectra of secondary antiprotons and potential correlations in the experimental data, so far not provided by the AMS-02 collaboration. In particular, to investigate the impact of cross-section uncertainties I will present the results of two different methods. In the first method, the uncertainties are taken into account by including a covariance matrix determined from nuclear cross-section measurements. The alternative approach is to perform a joint fit, simultaneously to cosmic-ray and cross-section data. I will show that the cross-section uncertainties have a small effect on the cosmic-ray fits. The inclusion of potential correlations in the data could have a much larger impact. I will discuss a method to determine and include possible benchmark models for the correlations in a data-driven approach.

Primary author

Michael Korsmeier (University of Turin)

Co-authors

Presentation materials