# Deep-learning Top Taggers or The End of QCD?

## TL;DR

This paper explores the use of convolutional neural networks for top quark tagging in LHC jet images, comparing their performance to traditional QCD-based methods, and finds comparable effectiveness.

## Contribution

It introduces a DeepTop CNN approach for top tagging and demonstrates its performance is comparable to established QCD-based methods.

## Key findings

- CNN-based top tagger achieves performance comparable to QCD-based methods.
- DeepTop approach is a promising new tool for multivariate hypothesis-based top tagging.
- Convolutional networks can effectively analyze jet images for particle identification.

## Abstract

Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.