Light-curve classification in massive variability surveys -- I. Microlensing

This paper exploits neural networks to provide a fast and automatic way to classify light curves in massive photometric data sets. As an example, we provide a working neural network that can distinguish microlensing light curves from other forms of variability, such as eruptive, pulsating, cataclysmic and eclipsing variable stars. The network has five input neurons, a hidden layer of five neurons and one output neuron. The five input variables for the network are extracted by spectral analysis from the light-curve data points and are optimized for the identification of a single, symmetric, microlensing bump. The output of the network is the posterior probability of microlensing.