在ATLAS实验中，不同ML方法应用于ttbar处于最终状态的事件分类的比较
Comparison of different ML methods applied to the classification of events with ttbar in the final state at the ATLAS experiment
摘要：
这篇文章描述了将不同的机器学习（ML）技术应用于物理分析案例的经验。所选择的用例是使用来自模拟事件存储库的数据对来自BSM或来自SM的顶-反顶事件进行分类。这些事件的特征由它们的运动学观测量来表示。最初的目标是比较不同的ML方法，以了解它们是否可以导致分类的改进，但这项工作也帮助我们通过改变超参数，使用不同的优化器，集成等来测试方法中的许多变化。
英文摘要：
This contribution describes the experience with the application of different Machine Learning (ML) techniques to a physics analysis case. The use case chosen is the classification of top-antitop events coming from BSM or from SM using data from a repository of simulated events. The features of these events are represented by their kinematic observables. The initial objective was to compare different ML methods in order to see whether they can lead to an improvement in the classification, but the work has also helped us to test many variations in the methods by changing hyper-parameters, using different optimisers, ensembles, etc. With this information we have been able to conduct a comparative study that is useful for ensuring as complete control as possible of the methodology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
