# Joint Contour Net Analysis for Feature Detection in Lattice Quantum Chromodynamics Data

## TL;DR

This paper applies multivariate topological algorithms, specifically the Joint Contour Net, to analyze Lattice QCD data, aiding physicists in feature detection, tracking, and understanding the dynamics of complex quantum systems.

## Contribution

It introduces the use of the Joint Contour Net for feature detection and temporal analysis in Lattice QCD data, a novel application in this domain.

## Key findings

- Effective detection of key features in Lattice QCD data
- Ability to track feature evolution over time
- Quantitative insights into the lifetime of objects in simulations

## Abstract

In this paper we demonstrate the use of multivariate topological algorithms to analyse and interpret Lattice Quantum Chromodynamics (QCD) data. Lattice QCD is a long established field of theoretical physics research in the pursuit of understanding the strong nuclear force. Complex computer simulations model interactions between quarks and gluons to test theories regarding the behaviour of matter in a range of extreme environments. Data sets are typically generated using Monte Carlo methods, providing an ensemble of configurations, from which observable averages must be computed. This presents issues with regard to visualisation and analysis of the data as a typical ensemble study can generate hundreds or thousands of unique configurations. We show how multivariate topological methods, such as the Joint Contour Net, can assist physicists in the detection and tracking of important features within their data in a temporal setting. This enables them to focus upon the structure and distribution of the core observables by identifying them within the surrounding data. These techniques also demonstrate how quantitative approaches can help understand the lifetime of objects in a dynamic system.