论文标题
具有深神经网络的矩阵元素回归 - 打破CPU屏障
Matrix Element Regression with Deep Neural Networks -- breaking the CPU barrier
论文作者
论文摘要
矩阵元素方法（MEM）是一种在对撞机实验中从测量事件中提取信息的强大方法。与基于大量实验数据建立的多元技术相比，MEM不依赖基于示例的学习阶段，而是直接利用我们对物理过程的了解。这是一个价格，无论是在复杂性和计算时间方面，都需要评估所考虑的每个事件和物理过程所需的快速变化功能的多维组成部分。就像Momemta软件包中所做的那样，可以通过优化集成来减轻这种情况，但是计算时间仍然是一个关注的问题，并且经常在全尺度分析中使用MEM非实践或不可能。我们在本文中调查了通过将MEM积分作为ANSATZ回归构建的深神经网络（DNN）的使用，尤其是在寻找新物理学时。
The Matrix Element Method (MEM) is a powerful method to extract information from measured events at collider experiments. Compared to multivariate techniques built on large sets of experimental data, the MEM does not rely on an examples-based learning phase but directly exploits our knowledge of the physics processes. This comes at a price, both in term of complexity and computing time since the required multi-dimensional integral of a rapidly varying function needs to be evaluated for every event and physics process considered. This can be mitigated by optimizing the integration, as is done in the MoMEMta package, but the computing time remains a concern, and often makes the use of the MEM in full-scale analysis unpractical or impossible. We investigate in this paper the use of a Deep Neural Network (DNN) built by regression of the MEM integral as an ansatz for analysis, especially in the search for new physics.
