# Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics

## TL;DR

This paper introduces new projection pursuit indexes based on scagnostics and maximum information coefficient to uncover complex relationships in physics data, demonstrated through simulated and gravitational wave astronomy data.

## Contribution

It develops novel indexes for projection pursuit that reveal complex bivariate patterns, extending existing tools for physics data analysis.

## Key findings

- Indexes perform well on simulated data
- Effective in revealing complex physics relationships
- Applicable to gravitational wave data

## Abstract

Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. Most indexes have been developed to detect departure from known distributions, such as normality, or to find separations between known groups. Here, we are interested in finding projections revealing potentially complex bivariate patterns, using new indexes constructed from scagnostics and a maximum information coefficient, with a purpose to detect unusual relationships between model parameters describing physics phenomena. The performance of these indexes is examined with respect to ideal behaviour, using simulated data, and then applied to problems from gravitational wave astronomy. The implementation builds upon the projection pursuit tools available in the R package, tourr, with indexes constructed from code in the R packages, scagnostics, minerva and mbgraphic.