一种带有事件重新加权的多元训练技术
A Multivariate Training Technique with Event Reweighting
摘要：
采用人工神经网络（ANN）和Boosted决策树（BDT），开发并测试了一种在多变量训练算法中采用的事件重新加权技术。基于ANN和BDT性能，将事件重加权训练与传统的等事件加权训练进行了比较。比较是在大型强子对撞机（LHC）的ATLAS实验的物理分析的背景下进行的，该实验将探索物质的基本性质和塑造我们宇宙的基本力量。我们证明，事件重新加权技术提供了一个无偏的方法，多变量训练的事件模式识别。
英文摘要：
An event reweighting technique incorporated in multivariate training algorithm has been developed and tested using the Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT). The event reweighting training are compared to that of the conventional equal event weighting based on the ANN and the BDT performance. The comparison is performed in the context of the physics analysis of the ATLAS experiment at the Large Hadron Collider (LHC), which will explore the fundamental nature of matter and the basic forces that shape our universe. We demonstrate that the event reweighting technique provides an unbiased method of multivariate training for event pattern recognition.
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。
