Jet Substructure Classification in High-Energy Physics with Deep Neural Networks

TL;DR

This paper demonstrates that deep neural networks can classify jet substructures in collider data effectively, matching or surpassing traditional expert-feature methods without requiring domain-specific feature engineering.

Contribution

The study introduces a deep neural network approach using locally-connected and fully-connected layers for jet classification, achieving competitive performance without expert features.

Findings

Neural networks match or outperform traditional methods.

Performance remains robust with pileup interactions.

Deep learning simplifies jet substructure analysis.

Abstract

At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet is due to a low-mass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools. The data collected by the detector can be treated as a two-dimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locally-connected and fully-connected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.