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