Deep Transfer Learning for Classification of Variable Sources

Understand

Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe light-curves of billons or more astronomical sources.

• This presents new challenges for identifying interesting and important types of variability.
• Collecting a sufficient number of labelled data for training is difficult, however, 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 dataset to another.