论文标题
研究神经网络的生存能力，作为在LHC等地图集等实验中检测异常检测的一种手段
Investigation Into the Viability of Neural Networks as a Means for Anomaly Detection in Experiments Like Atlas at the LHC
论文作者
论文摘要
数据是在大型强子对撞机的ATLAS实验中生成的，但是并非所有这些都一定都很有趣，因此我们如何处理所有这些数据，以及我们如何在无趣的Haystack中找到这些有趣的针头。通过异常检测过程可以解决此问题。在本文档中，对神经网络的生存能力的研究是在LHC诸如Atlas之类的实验中进行异常检测的一种手段，使用蒙特卡洛模拟数据研究了Darkmachines项目产生的不同类型的神经网络体系结构作为异常检测器的有效性。该数据旨在复制标准模型并超出标准模型事件。通过找到一个有效的模型，Atlas实验可以变得更加有效，更少的有趣事件将丢失。
Petabytes of data are generated at the Atlas experiment at the Large Hadron Collider however not all of it is necessarily interesting, so what do we do with all of this data and how do we find these interesting needles in an uninteresting haystack. This problem can possibly be solved through the process of anomaly detection. In this document, Investigation Into the Viability of Neural Networks as a Means for Anomaly Detection in Experiments Like Atlas at the LHC the effectiveness of different types of neural network architectures as anomaly detectors are researched using Monte Carlo simulated data generated by the DarkMachines project. This data is meant to replicate Standard Model and Beyond Standard Model events. By finding an effective model, the Atlas experiment can become more effective and fewer interesting events will be lost.
