  In the era of multi-messenger astronomy, early classification of photometric
alerts from wide-field and high-cadence surveys is a necessity to trigger
spectroscopic follow-ups. These classifications are expected to play a key role
in identifying potential candidates that might have a corresponding
gravitational wave (GW) signature. Machine learning classifiers using features
from parametric fitting of light curves are widely deployed by broker software
to analyze millions of alerts, but most of these algorithms require as many
points in the filter as the number of parameters to produce the fit, which
increases the chances of missing a short transient. Moreover, the classifiers
are not able to account for the uncertainty in the fits when producing the
final score. In this context, we present a novel classification strategy that
incorporates data-driven priors for extracting a joint posterior distribution
of fit parameters and hence obtaining a distribution of classification scores.
We train and test a classifier to identify kilonovae events which originate
from binary neutron star mergers or neutron star black hole mergers, among
simulations for the Zwicky Transient Facility observations with 19 other
non-kilonovae-type events. We demonstrate that our method can estimate the
uncertainty of misclassification, and the mean of the distribution of
classification scores as point estimate obtains an AUC score of 0.96 on
simulated data. We further show that using this method we can process the
entire alert steam in real-time and bring down the sample of probable events to
a scale where they can be analyzed by domain experts.
