通过非高斯性检测对撞机数据流形中的新物理信号
Non-Gaussianities in Collider Metric Binning
- 用事件对距离分布的非高斯性量化数据流形特征
- 距离箱内事件对数的方差与均值比随事件数平方根反比下降
- 可探测强子化过程和高能下流形对称性增强现象
用于严格定义两个事件间距离的度量已被用于研究粒子对撞机物理数据流形的性质。数据流形上成对距离的概率分布具有几乎必然唯一的特性，这为通过测量偏离零假设预测来搜寻和识别新物理提供了方法。为统计量化偏离程度，我们直接计算固定距离间隔内事件对落入某一箱的概率分布。该分布通常不是高斯分布，且箱内事件对数的均方差与均值之比随数据集事件数的平方根反比下降。若数据流形表现出某种增强对称性，则事件对数呈高斯分布，且相对于均值的波动随事件数增加而按倒数衰减。我们定义了一个稳健的非高斯性度量，用于分析距离分布的箱间统计特性，并在量子色动力学喷注的模拟数据中展示了对部分子到强子转变过程的敏感性，以及能量升高时事件流形展现出增强对称性的现象。
原文摘要 · Abstract (English)
Metrics for rigorously defining a distance between two events have been used to study the properties of the dataspace manifold of particle collider physics. The probability distribution of pairwise distances on this dataspace is unique with probability 1, and so this suggests a method to search for and identify new physics by the deviation of measurement from a null hypothesis prediction. To quantify the deviation statistically, we directly calculate the probability distribution of the number of event pairs that land in the bin a fixed distance apart. This distribution is not generically Gaussian and the ratio of the standard deviation to the mean entries in a bin scales inversely with the square-root of the number of events in the data ensemble. If the dataspace manifold exhibits some enhanced symmetry, the number of entries is Gaussian, and further fluctuations about the mean scale away like the inverse of the number of events. We define a robust measure of the non-Gaussianity of the bin-by-bin statistics of the distance distribution, and demonstrate in simulated data of jets from quantum chromodynamics sensitivity to the parton-to-hadron transition and that the manifold of events enjoys enhanced symmetries as their energy increases.
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