  Cataclysmic variables (CV) encompass a diverse array of accreting white dwarf
binary systems. Each class of CV represents a snapshot along an evolutionary
journey, one with the potential to trigger a type Ia supernova event. The study
of CVs offers valuable insights into binary evolution and accretion physics,
with the rarest examples potentially providing the deepest insights. However,
the escalating number of detected transients, coupled with our limited capacity
to investigate them all, poses challenges in identifying such rarities. Machine
Learning (ML) plays a pivotal role in addressing this issue by facilitating the
categorisation of each detected transient into its respective transient class.
Leveraging these techniques, we have developed a two-stage pipeline tailored to
the ZTF transient alert stream. The first stage is an alerts filter aimed at
removing non-CVs, while the latter is an ML classifier produced using XGBoost,
achieving a macro average AUC score of 0.92 for distinguishing between CV
classes. By utilising the Generative Topographic Mapping algorithm with
classifier posterior probabilities as input, we obtain representations
indicating that CV evolutionary factors play a role in classifier performance,
while the associated feature maps present a potent tool for identifying the
features deemed most relevant for distinguishing between classes.
Implementation of the pipeline in June 2023 yielded 51 intriguing candidates
that are yet to be reported as CVs or classified with further granularity. Our
classifier represents a significant step in the discovery and classification of
different CV classes, a domain of research still in its infancy.
