  During the last decade, a considerable amount of effort has been made to
classify variable stars using different machine learning techniques. Typically,
light curves are represented as vectors of statistical descriptors or features
that are used to train various algorithms. These features demand big
computational powers that can last from hours to days, making impossible to
create scalable and efficient ways of automatically classifying variable stars.
Also, light curves from different surveys cannot be integrated and analyzed
together when using features, because of observational differences. For
example, having variations in cadence and filters, feature distributions become
biased and require expensive data-calibration models. The vast amount of data
that will be generated soon make necessary to develop scalable machine learning
architectures without expensive integration techniques. Convolutional Neural
Networks have shown impressing results in raw image classification and
representation within the machine learning literature. In this work, we present
a novel Deep Learning model for light curve classification, mainly based on
convolutional units. Our architecture receives as input the differences between
time and magnitude of light curves. It captures the essential classification
patterns regardless of cadence and filter. In addition, we introduce a novel
data augmentation schema for unevenly sampled time series. We test our method
using three different surveys: OGLE-III; Corot; and VVV, which differ in
filters, cadence, and area of the sky. We show that besides the benefit of
scalability, our model obtains state of the art levels accuracy in light curve
classification benchmarks.
