Morphology for Jet Classification

TL;DR

This paper introduces a novel jet tagging method using Minkowski Functionals analyzed through a neural network, capturing geometric structures at various scales, and demonstrating improved performance with limited data compared to traditional CNNs.

Contribution

The paper presents a new morphological approach to jet classification using Minkowski Functionals, linking geometric measures with neural networks, and showing advantages in data efficiency and computational cost.

Findings

MF-based neural network achieves comparable performance to CNNs.

The method performs better with limited training data.

MF analysis captures IRC-unsafe features effectively.

Abstract

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.