Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

TL;DR

Deep-HiTS introduces a rotation invariant CNN for classifying astronomical transient images, significantly outperforming traditional feature-based methods and aiding future large-scale surveys like LSST.

Contribution

First application of CNNs for astronomical transient detection, demonstrating superior performance over feature engineering approaches.

Findings

CNN reduces classification error by nearly 50%.

Decreases misclassified real transients by about 20%.

Effective for processing data from next-generation telescopes.

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 https://github.com/guille-c/Deep-HiTS.