论文标题
学习超高能宇宙射线的组成
Learning the Composition of Ultra High Energy Cosmic Rays
论文作者
论文摘要
我们对Pierre螺旋钻的开放数据进行统计推断，以首次在不同能量的宇宙射线的全部质量组成。使用宇宙射线淋浴的纵向电磁曲线，特别是它们的峰值深度$ x _ {\ rm max} $，我们采用$ x _ {\ rm max} $分布的中央矩作为特征，以区分不同的淋浴成分。我们发现，最初的瞬间已经需要最相关的信息来推断主要的宇宙射线质量谱。我们的方法基于未扣除的可能性，使我们能够始终考虑由于有限数据集（测量和模拟）以及系统效应而导致的统计不确定性来源。最后，我们提供了大气淋浴模拟代码中可用的不同高能量辐射相互作用模型的定量比较。
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.
