Title: Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection,
Abstract: We introduce Deep-HiTS, a rotation invariant convolutional neural network
(CNN) model for classifying images of transients candidates into artifacts or
real sources for the High cadence Transient Survey (HiTS). CNNs have the
advantage of learning the features automatically from the data while achieving
high performance. We compare our CNN model against a feature engineering
approach using random forests (RF). We show that our CNN significantly
outperforms the RF model reducing the error by almost half. Furthermore, for a
fixed number of approximately 2,000 allowed false transient candidates per
night we are able to reduce the miss-classified real transients by
approximately 1/5. To the best of our knowledge, this is the first time CNNs
have been used to detect astronomical transient events. Our approach will be
very useful when processing images from next generation instruments such as the
Large Synoptic Survey Telescope (LSST). We have made all our code and data
available to the community for the sake of allowing further developments and
comparisons at this https URL.