Finding rare classes in large datasets: the case of polluted white dwarfs from Gaia XP spectra

Abstract

The Gaia mission's third data release recorded low-resolution spectra for about 100 000 white dwarf candidates. A small subset of these spectra show evidence of characteristic broad Ca II absorption features, implying the accretion of rocky material by so-called polluted white dwarfs -- important probes of the composition of exoplanetary material. Several supervised and unsupervised data-intensive methods have recently been applied to identify polluted white dwarfs from the Gaia spectra. We present a comparison of these methods, along with the first application of $t$-distributed stochastic neighbour embedding ($t$SNE) to this dataset. We find that $t$SNE outperforms the similar technique Uniform Manifold Approximation and Projection (UMAP), isolating over 50% more high-confidence polluted candidates, including 39 new candidates which are not selected by any other method investigated and which have not been observed at higher resolution. Supervised methods benefit greatly from data labels provided by earlier works, selecting many known polluted white dwarfs which are missed by unsupervised methods. Our work provides a useful case study in the selection of members of rare classes from a large, sporadically labelled dataset, with applications across astronomy.