Application of deep learning in top pair and single top quark production at the LHC

We demonstrate the performance of a very efficient top tagger applies on hadronically decaying boosted top quark pairs as signal based on deep neural network algorithms working with Lorentz Layer and the Minkowski metric. Due to limited computing resources, we could show only the receiver ordering characteristic curve, accuracy and loss which illustrates the trade-off between signal acceptance against huge QCD multi-jet background acceptance. Alternatively, we also report the modern machine learning approaches and apply multivariate technique on single top quark production through weak interaction at √(s)= 14 TeV proton-proton Collider to demonstrate its observability against the most relevant Standard Model backgrounds through the techniques of boosted decision tree (BDT), likelihood and multilayer perceptron (MLP). The analysis is trained to observe the performance of classifiers in comparison with the conventional cut based and counting approach.