  Astronomy light curves are sparse, gappy, and heteroscedastic. As a result
standard time series methods regularly used for financial and similar datasets
are of little help and astronomers are usually left to their own instruments
and techniques to classify light curves. A common approach is to derive
statistical features from the time series and to use machine learning methods,
generally supervised, to separate objects into a few of the standard classes.
In this work, we transform the time series to two-dimensional light curve
representations in order to classify them using modern deep learning
techniques. In particular, we show that convolutional neural networks based
classifiers work well for broad characterization and classification. We use
labeled datasets of periodic variables from CRTS survey and show how this opens
doors for a quick classification of diverse classes with several possible
exciting extensions.
