Kavli Affiliate: Mihoko M. Nojiri
| First 5 Authors: Sung Hak Lim, Mihoko M. Nojiri, , ,
| Summary:
We introduce a jet tagger based on a neural network analyzing the Minkowski
Functionals (MFs) of pixellated jet images. The MFs are geometric measures of
binary images, and they can be regarded as a generalization of the particle
multiplicity, which is an important quantity in jet tagging. Their changes by
dilation encode the jet constituents’ geometric structures that appear at
various angular scales. We explicitly show that this analysis using the MFs
together with mathematical morphology can be considered a constrained
convolutional neural network (CNN). Conversely, CNN could model the MFs in a
certain limit, and we show their correlation in the example of tagging
semi-visible jets emerging from the strong interaction of a hidden valley
scenario. The MFs are independent of the IRC-safe observables commonly used in
jet physics. We combine this morphological analysis with an IRC-safe relation
network which models two-point energy correlations. While the resulting network
uses constrained input parameters, it shows comparable dark jet and top jet
tagging performances to the CNN. The architecture has significant computational
advantages when the available data is limited. We show that its tagging
performance is much better than that of the CNN with a small number of training
samples. We also qualitatively discuss their parton-shower model dependency.
The results suggest that the MFs can be an efficient parameterization of the
IRC-unsafe feature space of jets.
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