Finding rare classes in large data sets: the case of polluted white dwarfs from <i>Gaia</i> 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 the 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 (tSNE) to this data set. We find that tSNE outperforms the similar technique Uniform Manifold Approximation and Projection, isolating over 50 per cent 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 data set, with applications across astronomy.