论文标题
在ATLAS实验中，在最终状态下使用TTBAR分类的不同ML方法的比较
Comparison of different ML methods applied to the classification of events with ttbar in the final state at the ATLAS experiment
论文作者
论文摘要
这项贡献描述了不同机器学习（ML）技术在物理分析案例中的经验。选择的用例是使用模拟事件存储库中的数据分类来自BSM或SM。这些事件的特征由它们的运动观测值表示。最初的目的是比较不同的ML方法，以查看它们是否可以改善分类，但是这项工作还通过更改超参数，使用不同的优化者，合奏等来帮助我们测试方法的许多变化。通过此信息，我们可以进行一项可用于完全控制该方法的比较研究。
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.
