# Scrutinizing the evidence for dark matter in cosmic-ray antiprotons

## TL;DR

This study critically examines the robustness of potential dark matter signals in cosmic-ray antiprotons by analyzing uncertainties in production cross sections and data correlations, finding that data correlations significantly impact the significance of the signal.

## Contribution

The paper systematically evaluates the effects of cross-section uncertainties and data correlations on dark matter signal significance in cosmic-ray antiproton data, highlighting the importance of covariance information.

## Key findings

- Cross-section uncertainties have a small effect on the dark matter signal significance.
- Including data correlations can increase the signal significance from 3σ to above 5σ.
- Proper covariance data is crucial for accurate interpretation of cosmic-ray measurements.

## 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 $3\sigma$. 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 $5\sigma$. Our analysis demonstrates the importance of providing the covariance of the experimental data, which is needed to fully exploit their potential.