论文标题
神经嵌入：学习物理数据流的嵌入
Neural Embedding: Learning the Embedding of the Manifold of Physics Data
论文作者
论文摘要
在本文中，我们提出了一种将标准结构嵌入物理数据歧管的方法，该方法具有更简单的指标，例如欧几里得和双曲线空间。然后，我们证明这可能是许多应用程序数据分析管道中的有力一步。在大型强子对撞机上使用逐渐更现实的模拟碰撞，我们表明这种嵌入方法了解了潜在的潜在结构。借助欧几里得空间中的体积概念，我们首次提供了可行的解决方案，用于量化对撞机物理学中模型不可知论搜索算法的真实搜索能力（即异常检测）。最后，我们讨论了如何采用本文中提出的思想来解决许多实际挑战，这些挑战需要从复杂的高维数据集中提取物理有意义的表示形式。
In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.
