# JEDI-net: a jet identification algorithm based on interaction networks

## TL;DR

JEDI-net is a novel interaction network-based algorithm for jet identification at the LHC, effectively distinguishing heavy particle decays from ordinary jets without complex pre-processing.

## Contribution

The paper introduces JEDI-net, a new interaction network architecture that improves jet classification performance with fewer parameters and no special input handling.

## Key findings

- Achieves better classification accuracy than existing methods.
- Requires less model complexity and pre-processing.
- Demonstrates robustness across different detector geometries.

## Abstract

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.