Pulling Out All the Tops with Computer Vision and Deep Learning

TL;DR

This paper enhances a CNN-based top quark jet tagger using deep learning techniques, achieving significantly better background rejection than traditional methods across various jet conditions.

Contribution

It introduces several improvements to the DeepTop CNN top tagger, resulting in a more effective deep learning-based jet classification tool for LHC applications.

Findings

CNN top tagger outperforms BDTs by a factor of 2-3 in background rejection

Achieves a background rejection factor of 500 at 50% efficiency for high-energy jets

Extensions to classify other jet types are straightforward and promising.

Abstract

We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) -- to build a highly effective boosted top tagger. Previous work (the "DeepTop" tagger of Kasieczka et al) has shown that a CNN-based top tagger can achieve comparable performance to state-of-the-art conventional top taggers based on high-level inputs. Here, we introduce a number of improvements to the DeepTop tagger, including architecture, training, image preprocessing, sample size and color pixels. Our final CNN top tagger outperforms BDTs based on high-level inputs by a factor of $\sim 2$--3 or more in background rejection, over a wide range of tagging efficiencies and fiducial jet selections. As reference points, we achieve a QCD background rejection factor of 500 (60) at 50\% top tagging efficiency for fully-merged (non-merged) top jets with $p_T$ in the 800--900 GeV (350--450 GeV) range. Our CNN can also be straightforwardly extended to the classification of other types of jets, and the lessons learned here may be useful to others designing their own deep NNs for LHC applications.