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arXiv 2212.04760astro-ph.HEhep-ph
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超高能宇宙线的成分学习

Learning the Composition of Ultra High Energy Cosmic Rays

Blaz Bortolato, Jernej F. Kamenik, Michele Tammaro

AI总结:

本研究利用Pierre Auger开放数据,通过$X_{\ m max}$分布的中心矩与无分箱似然方法,首次推断不同能量下超高能宇宙线的完整质量成分,并对比了不同高能强子相互作用模型。

摘要:

我们对Pierre Auger(皮埃尔·俄歇)开放数据开展统计推断,首次识别出不同能量下宇宙线的完整质量成分。我们以宇宙线簇射的纵向电磁剖面(尤其是其极大深度$X_{\rm max}$)为研究对象,采用$X_{\rm max}$分布的中心矩作为特征来区分不同的簇射成分。研究发现,前几阶矩已包含推断原初宇宙线质谱的最相关信息。我们的方法基于无分箱似然,能够一致地考量有限数据集(实测与模拟数据集)带来的统计不确定性来源以及系统效应。最后,我们对大气簇射模拟代码中可用的不同高能强子相互作用模型进行了定量比较。

英文摘要:

We apply statistical inference on the Pierre Auger Open Data to discern for the first time the full mass composition of cosmic rays at different energies. Working with longitudinal electromagnetic profiles of cosmic ray showers, in particular their peaking depths $X_{\rm max}$, we employ central moments of the $X_{\rm max}$ distributions as features to discriminate between different shower compositions. We find that already the first few moments entail the most relevant information to infer the primary cosmic ray mass spectrum. Our approach, based on an unbinned likelihood, allows us to consistently account for sources of statistical uncertainties due to finite datasets, both measured and simulated, as well as systematic effects. Finally, we provide a quantitative comparison of different high energy hadronic interaction models available in the atmospheric shower simulation codes.

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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