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Recently, machine learning methods presented a viable solution for automatedclassification of image-based data in various research fields and businessapplications. Scientists require a fast and reliable solution to be able tohandle the always growing enormous amount of data in astronomy. However, so farastronomers have been mainly classifying variable star light curves based onvarious pre-computed statistics and light curve parameters. In this work we usean image-based Convolutional Neural Network to classify the different types ofvariable stars. We used images of phase-folded light curves from the OGLE-IIIsurvey for training, validating and testing and used OGLE-IV survey as anindependent data set for testing. After the training phase, our neural networkwas able to classify the different types between 80 and 99%, and 77-98%accuracy for OGLE-III and OGLE-IV, respectively.

Image-based Classification of Variable Stars: First Results from Optical Gravitational Lensing Experiment Data