Arbor, a new approach of the Particle Flow Algorithm

TL;DR

Arbor is a novel Particle Flow Algorithm framework leveraging high-granularity calorimeter data to accurately reconstruct shower substructures and improve event reconstruction in future collider experiments.

Contribution

It introduces a new tree topology-based Particle Flow Algorithm that effectively separates nearby showers and tags sub-shower structures, advancing calorimeter data analysis.

Findings

Successfully separates nearby showers in simulated and test beam data.

Achieves jet energy resolution comparable to the best existing algorithms.

Effectively tags sub-shower structures like charged particle trajectories.

Abstract

The granularity of calorimeter has been revolutionary boosted for future collider experiments. The calorimeter has been pushed to a stage that the sub structure of showers especially hadronic showers can be recorded to a high precision. New reconstruction algorithms are expected from these informations. Following the idea that shower follows the topology of the tree, we developed Arbor, a Particle Flow Algorithm framework. Tested on both simulated data and test beam data, it can successfully separate nearby showers. It has comparable jet energy resolution the best PFA algorithm for International Linear Collider. More importantly, Arbor successfully tags the sub shower structure such as the trajectory of charged particles generated in shower cascade, enabling new approaches for event reconstruction with high granularity calorimeter.