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arXiv 1903.09545astro-ph.HEhep-ph
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用机器学习方法研究cosmic ray反质子流中的暗物质信号

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

Su-Jie Lin, Xiao-Jun Bi, Peng-Fei Yin

摘要:

我们研究了AMS-02宇宙反质子流对暗物质(DM)信号的影响。在不同的传播模型和强子相互作用模型下,对数据进行了全局拟合。综合考虑了来自宇宙射线的注入谱、传播效应和太阳调制的不确定性。由于在拟合中需要考察具有多个自由参数的扩展参数区域,因此采用机器学习方法来保持现实的时间成本。我们发现在拟合过程中考虑的各种效应是相互影响的,其中强子相互作用模型是影响拟合结果的最重要因素。在大多数强子相互作用和CR传播模型中,除了EPOS-LHC相互作用模型需要DM质量在$1\,\mathrm{TeV}$附近的DM信号大于$3σ$之外,没有发现DM信号的显著性大于$2σ$。对于扩散再加速传播模型,在$100\,\mathrm{GeV}$附近存在质量非常显著的DM信号。然而,如果我们在分析中采用电荷依赖的太阳调制电势,则信号变得小于$1σ$。

英文摘要:

We investigate the implications on the dark matter (DM) signal from the AMS-02 cosmic antiproton flux. Global fits to the data are performed under different propagation and hadronic interaction models. The uncertainties from the injection spectrum, propagation effects and solar modulation of the cosmic rays are taken into account comprehensively. Since we need to investigate extended parameter regions with multiple free parameters in the fit, the machine learning method is adopted to maintain a realistic time cost. We find all the effects considered in the fitting process interplay with each other, among which the hadronic interaction model is the most important factor affecting the result. In most hadronic interaction and CR propagation models no DM signal is found with significance larger than $2σ$ except that the EPOS-LHC interaction model requires a more than $3σ$ DM signal with DM mass around $1\,\mathrm{TeV}$. For the diffusive reacceleration propagation model there is a highly significant DM signal with mass around $100\,\mathrm{GeV}$. However, the signal becomes less than $1σ$ if we take a charge dependent solar modulation potential in the analysis.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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