Interaction networks for the identification of boosted $H\to b\overline{b}$ decays

TL;DR

This paper presents an interaction network-based algorithm for identifying high-energy Higgs bosons decaying into bottom quark pairs, significantly improving jet classification performance in collider data analysis.

Contribution

The paper introduces a novel interaction network model that leverages particle and vertex features to enhance Higgs boson decay identification in collider experiments.

Findings

Achieves significant improvement over existing algorithms.

Trained on realistic simulated LHC collision data.

Effectively distinguishes Higgs decays from background jets.

Abstract

We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.