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

TL;DR

This paper demonstrates the effectiveness of deep neural networks and other machine learning methods in identifying top quark events at the LHC, showing significant improvements over traditional techniques.

Contribution

It introduces a deep learning-based top quark tagger and compares its performance with other machine learning approaches and conventional methods in high-energy physics.

Findings

Deep neural networks significantly improve top quark tagging performance.

Machine learning approaches outperform traditional cut-based methods.

Boosted top quark events are identified with high efficiency using limited computing resources.

Abstract

We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at $\sqrt{s}=$14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach.