Galaxy mergers sample

Moreover, we investigate the use of transfer learning in conjunction with CNNs by retraining networks first trained on pictures of everyday objects. We test the hypothesis that transfer learning is useful for improving classification performance for small training sets.

This would make transfer learning useful for finding rare objects in astronomical imaging data sets. We find that these deep learning methods perform significantly better than current state-of-the-art merger detection methods based on non-parametric systems such as CAS and GM20. Our method is end-to-end and robust to image noise and distortions; it can be applied directly without image preprocessing.