Speaker
Sreedevi Varma
Description
Machine Learning techniques have been widely used in different applications in high energy physics. In this talk I would like to speak about two different machine learning algorithms used to classify signal and background jets. We compare the performance of a convolutional neural network (CNN) trained on jet images with dense neural networks (DNNs) trained on n-subjettiness variables to study the distinguishing power of these two separate techniques applied to top quark decays. We obtain a comparable results from both techniques which suggest that the underlying physics learned using these neural networks are the same.
