Kavli Affiliate: Mihoko M. Nojiri
| First 5 Authors: Amit Chakraborty, Sung Hak Lim, Mihoko M. Nojiri, Michihisa Takeuchi,
| Summary:
Deep neural networks trained on jet images have been successful in
classifying different kinds of jets. In this paper, we identify the crucial
physics features that could reproduce the classification performance of the
convolutional neural network in the top jet vs. QCD jet classification. We
design a neural network that considers two types of substructural features:
two-point energy correlations, and the IRC unsafe counting variables of a
morphological analysis of jet images. The new set of IRC unsafe variables can
be described by Minkowski functionals from integral geometry. To integrate
these features into a single framework, we reintroduce two-point energy
correlations in terms of a graph neural network and provide the other features
to the network afterward. The network shows a comparable classification
performance to the convolutional neural network. Since both networks are using
IRC unsafe features at some level, the results based on simulations are often
dependent on the event generator choice. We compare the classification results
of Pythia 8 and Herwig 7, and a simple reweighting on the distribution of IRC
unsafe features reduces the difference between the results from the two
simulations.
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