论文标题
使用机器学习的高能量粒子物理数据的反卷积
Deconvolution of the High Energy Particle Physics Data with Machine Learning
论文作者
论文摘要
提出了一种使用机器学习技术纠正涂抹效果的方法。与高能粒子物理学中的标准反卷积方法相比，该方法可以使用多个重建变量来预测逐个事件的未切换数量的值。在这项特定的研究中，反卷积被解释为分类问题，并且对神经网络（NN）进行了训练，可以反驳用Madgraph和Pythia8 Monte Carlo事件发生器产生的Z玻色子不变质量谱，以证明原则。提出了从机器学习方法获得的结果，并将其与传统方法获得的结果进行了比较。
A method for correcting smearing effects using machine learning technique is presented. Compared to the standard deconvolution approaches in high energy particle physics, the method can use more than one reconstructed variable to predict the value of unsmeared quantity on an event-by-event basis. In this particular study, deconvolution is interpreted as a classification problem, and neural networks (NN) are trained to deconvolute the Z boson invariant mass spectrum generated with MadGraph and pythia8 Monte Carlo event generators in order to prove the principle. Results obtained from the machine learning method is presented and compared with the results obtained with traditional methods.
