  In this work we introduce group-equivariant self-attention models to address
the problem of explainable radio galaxy classification in astronomy. We
evaluate various orders of both cyclic and dihedral equivariance, and show that
including equivariance as a prior both reduces the number of epochs required to
fit the data and results in improved performance. We highlight the benefits of
equivariance when using self-attention as an explainable model and illustrate
how equivariant models statistically attend the same features in their
classifications as human astronomers.
