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.
Highlights
SignalSignal probability2.2 Network architectureTo identify a suitable DeepTop network architecture, we scan over several possible realizations or hyper-parameters
For this paper we focus on the range pT,fat = 350 . . . 450 GeV, such that all top decay products can be captured in the fat jet
To benchmark the performance of our DeepTop deep neural networks (DNN), we compare its receiver operator characteristic (ROC) curve with standard Boosted Decision Trees based on the C/A jets using SoftDrop combined with N -subjettiness
Summary
Top tagging is a typical (binary) classification problem. Given a set of variables {xi} we predict a signal or background label y. The final classification of the boosted decision tree (BDT) is based on the vote of all classifiers, and leads to an increased performance and more stable results. In image and pattern recognition convolutional networks (ConvNets) have shown impressive results. Their main feature is the structure of the input, where for example in a two-dimensional image the information of neighboring pixels should be correlated. If we attempt to extract features in the image with standard DNN and fully connected neurons in each layer to all pixels, the construction scales poorly with the dimensionality of the image. While the convolution layers allow for the identification of features in the image, the actual classification is performed by the DNN. The machine learning side of our comparison will be based on ConvNets
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