  Microlensing is a powerful tool for discovering cold exoplanets, and the The
Roman Space Telescope microlensing survey will discover over 1000 such planets.
Rapid, automated classification of Roman's microlensing events can be used to
prioritize follow-up observations of the most interesting events. Machine
learning is now often used for classification problems in astronomy, but the
success of such algorithms can rely on the definition of appropriate features
that capture essential elements of the observations that can map to parameters
of interest. In this paper, we introduce tools that we have developed to
capture features in simulated Roman light curves of different types of
microlensing events, and evaluate their effectiveness in classifying
microlensing light curves. These features are quantified as parameters that can
be used to decide the likelihood that a given light curve is due to a specific
type of microlensing event. This method leaves us with a list of parameters
that describe features like the smoothness of the peak, symmetry, the number of
peaks, and width and height of small deviations from the main peak. This will
allow us to quickly analyze a set of microlensing light curves and later use
the resulting parameters as input to machine learning algorithms to classify
the events.
