Deep transfer learning for the classification of variable sources

Abstract

Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe the light curves of billions or more astronomical sources. This presents new challenges for identifying interesting and important types of variability. Collecting a sufficient amount of labeled data for training is difficult, especially in the early stages of a new survey. Here we develop a single-band light-curve classifier based on deep neural networks and use transfer learning to address the training data paucity problem by conveying knowledge from one data set to another. First we train a neural network on 16 variability features extracted from the light curves of OGLE and EROS-2 variables. We then optimize this model using a small set (e.g., 5%) of periodic variable light curves from the ASAS data set in order to transfer knowledge inferred from OGLE and EROS-2 to a new ASAS classifier. With this we achieve good classification results on ASAS, thereby showing that knowledge can be successfully transferred between data sets. We demonstrate similar transfer learning using HIPPARCOS and ASAS-SN data. We therefore find that it is not necessary to train a neural network from scratch for every new survey; rather, transfer learning can be used, even when only a small set of labeled data is available in the new survey.