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We investigate the implications on the dark matter (DM) signal from theAMS-02 cosmic antiproton flux. Global fits to the data are performed underdifferent propagation and hadronic interaction models. The uncertainties fromthe injection spectrum, propagation effects and solar modulation of the cosmicrays are taken into account comprehensively. Since we need to investigateextended parameter regions with multiple free parameters in the fit, themachine learning method is adopted to maintain a realistic time cost. We findall the effects considered in the fitting process interplay with each other,among which the hadronic interaction model is the most important factoraffecting the result. In most hadronic interaction and CR propagation models noDM signal is found with significance larger than $2\sigma$ except that theEPOS-LHC interaction model requires a more than $3\sigma$ DM signal with DMmass around $1\,\mathrm{TeV}$. For the diffusive reacceleration propagationmodel there is a highly significant DM signal with mass around$100\,\mathrm{GeV}$. However, the signal becomes less than $1\sigma$ if we takea charge dependent solar modulation potential in the analysis.

Investigating the dark matter signal in the cosmic ray antiproton flux with the machine learning method