利用神经网络改善ttbar事件的组合模糊度
Improving Combinatorial Ambiguities of ttbar Events Using Neural Networks
摘要：
提出了一种解决Tevatron对撞机上发生的\ttbar轻子+喷流事件中组合问题的方法。通过将多个信息到人工神经网络，我们介绍了一种新的事件重建方法，这样的事件。我们发现，这种方法显着减少了组合的模糊性的数量。与经典的重建方法相比，我们的方法提供了显着更高的纯度相同的效率。我们举例说明了重建的实际顶夸克质量和前后向不对称测量的可观测量。蒙特卡罗研究表明，我们的方法提供了有意义的改进，在顶夸克测量使用相同的数据量的其他方法。
英文摘要：
We present a method for resolving the combinatorial issues in the \ttbar lepton+jets events occurring at the Tevatron collider. By incorporating multiple information into an artificial neural network, we introduce a novel event reconstruction method for such events. We find that this method significantly reduces the number of combinatorial ambiguities. Compared to the classical reconstruction method, our method provides significantly higher purity with same efficiency. We illustrate the reconstructed observables for the realistic top-quark mass and the forward-backward asymmetry measurements. A Monte Carlo study shows that our method provides meaningful improvements in the top-quark measurements using same amount of data as other methods.
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。
