A Machine Learning Classifier for Microlensing in Wide-Field Surveys
  While microlensing is very rare, occurring on average once per million stars
observed, current and near-future surveys are coming online with the capability
of providing photometry of almost the entire visible sky to depths up to R ~ 22
mag or fainter every few days, which will contribute to the detection of black
holes and exoplanets through follow-up observations of microlensing events.
Based on galactic models, we can expect microlensing events across a vastly
wider region of the galaxy, although the cadence of these surveys (2-3 per day
) is lower than traditional microlensing surveys, making efficient detection a
challenge. Rapid advances are being made in the utility of time-series data to
detect and classify transient events in real-time using very high data-rate
surveys, but limited work has been published regarding the detection of
microlensing events, particularly for when the data streams are of relatively
low-cadence. In this research, we explore the utility of a Random Forest
algorithm for identifying microlensing signals using time-series data, with the
goal of creating an efficient machine learning classifier that can be applied
to search for microlensing in wide-field surveys even with low-cadence data. We
have applied and optimized our classifier using the OGLE-II microlensing
dataset, in addition to testing with PTF/iPTF survey data and the currently
operating ZTF, which applies the same data handling infrastructure that is
envisioned for the upcoming LSST.
