Abstract

We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference images. This is the first time that sequences of images are used directly for the classification of variable objects in astronomy. The second contribution of this work is the image simulation process. We generate synthetic image sequences which take into account the instrumental and observing conditions, obtaining a realistic, unevenly sampled, and variable noise set of movies for each astronomical object. The simulated data set is used to train our RCNN classifier. This approach allows us to generate data sets to train and test our RCNN model for different astronomical surveys and telescopes. Moreover, using a simulated data set is faster and more adaptable to different surveys and classification tasks. We aim to build a simulated data set whose distribution is close enough to the real data set, so that fine tuning could match the distributions. To test the RCNN classifier trained with the synthetic data set, we used real-world data from the High cadence Transient Survey (HiTS), obtaining an average recall of 85%, improved to 94% after performing fine tuning with 10 real samples per class. We compare the results of our RCNN model with those of a light curve random forest classifier. The proposed RCNN with fine tuning has a similar performance on the HiTS data set compared to the light curve random forest classifier, trained on an augmented training set with 10 real samples per class. The RCNN approach presents several advantages in an alert stream classification scenario, such as a reduction of the data pre-processing, faster online evaluation, and easier performance improvement using a few real data samples. The results obtained encourage us to use the proposed method for astronomical alert broker systems that will process alert streams generated by new telescopes such as the Large Synoptic Survey Telescope.