论文标题
通过Voronoi和Delaunay Tessellations在LHC数据中找到WOMBLING界限
Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
论文作者
论文摘要
我们解决了在通用泊松点过程生成的点数据中找到误边界的问题，该过程的特定示例是分布在最终状态签名的相空间中的LHC事件样本，其中某些新物理学创建了wombling边界。我们讨论了点数据的Voronoi和Delaunay Tessellations估算局部梯度的使用，并研究了通过减少统计噪声来锐化边界的方法。传统的Wombling算法的结果是一组具有相对较大梯度的边界细胞候选物，然后必须对其空间特性进行审查以构建边界并评估其重要性。在这里，我们提出了一种替代方法，我们同时形成并评估所有可能的边界的重要性，从总梯度通量角度来看。我们用几个玩具示例说明了我们的方法，这些示例都有直界和弯曲边界，数据中存在不同量的信号。
We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics. We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then be scrutinized in order to construct the boundary and evaluate its significance. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data.
