  Machine learning techniques are used for treating jets as images to explore
the performance of boosted top quark tagging. Tagging performances are studied
in both hadronic and leptonic channels of top quark decay, employing a
convolutional neural network (CNN) based technique along with boosted decision
trees (BDT). This computer vision approach is also applied to distinguish
between left and right polarized top quarks. In this context, an experimentally
measurable asymmetry variable is proposed to estimate the polarization. Results
indicate that the CNN based classifier is more sensitive to top quark
polarization than the standard kinematic variables. It is observed that the
overall tagging performance in the leptonic channel is better than the hadronic
case, and the former also serves as a better probe for studying polarization.
