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The latest Gaia data release in July 2022, DR3, added a number of importantdata products to those available in earlier releases, including radial velocitydata, information on stellar multiplicity and XP spectra of a selected sampleof stars. While the normal Gaia photometry (G, GBP and GRP bands) andastrometry can be used to identify white dwarfs with high confidence, it ismuch more difficult to parameterise the stars and determine the white dwarfspectral type from this information alone. The availability of the XP spectraand synthetic photometry presents an opportunity for more detailed spectralclassification and measurement of effective temperature and surface gravity ofGaia white dwarfs. A magnitude limit of G < 17.6 was applied to the routineproduction of XP spectra for Gaia sources, which would have excluded most whitedwarfs. We created a catalogue of 100,000 high-quality white dwarfidentifications for which XP spectra were processed, with a magnitude limit ofG < 20.5. Synthetic photometry was computed for all these stars, from the XPspectra, in Johnson, SDSS and J-PAS, published as the Gaia Synthetic PhotometryCatalogue - White Dwarfs (GSPC-WD). We have now taken this catalogue andapplied machine learning techniques to provide a classification of all thestars from the XP spectra. We have then applied an automated spectral fittingprogramme, with chi-squared minimisation, to measure their physical parameters(effective temperature and log g) from which we can estimate the white dwarfmasses and radii. We present the results of this work, demonstrating the powerof being able to classify and parameterise such a large sample of 100, 000stars. We describe what we can learn about the white dwarf population from thisdata set. We also explore the uncertainties in the process and the limitationsof the data set.

Classification and parameterisation of a large Gaia sample of white dwarfs using XP spectra