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Projection pursuit is used to find interesting low-dimensional projections ofhigh-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 areinterested in finding projections revealing potentially complex bivariatepatterns, using new indexes constructed from scagnostics and a maximuminformation coefficient, with a purpose to detect unusual relationships betweenmodel parameters describing physics phenomena. The performance of these indexesis examined with respect to ideal behaviour, using simulated data, and thenapplied to problems from gravitational wave astronomy. The implementationbuilds upon the projection pursuit tools available in the R package, tourr,with indexes constructed from code in the R packages, scagnostics, minerva andmbgraphic.

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