Using transfer learning to detect galaxy mergers

TL;DR

This paper demonstrates that deep convolutional neural networks with transfer learning significantly improve the automatic detection of galaxy mergers, outperforming traditional methods and being robust to noise.

Contribution

It introduces a CNN-based method with transfer learning for galaxy merger detection, showing improved accuracy and robustness over existing techniques.

Findings

Deep CNNs outperform traditional nonparametric methods.

Transfer learning improves classification accuracy, especially with small datasets.

The method is robust to noise and distortions, enabling direct application without preprocessing.

Abstract

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. 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 datasets. We find that these deep learning methods perform significantly better than current state-of-the-art merger detection methods based on nonparametric systems like CAS and GM$_{20}$. Our method is end-to-end and robust to image noise and distortions; it can be applied directly without image preprocessing. We also find that transfer learning can act as a regulariser in some cases, leading to better overall classification accuracy ($p = 0.02$). Transfer learning on our full training set leads to a lowered error rate from 0.038 $\pm$ 1 down to 0.032 $\pm$ 1, a relative improvement of 15%. Finally, we perform a basic sanity-check by creating a merger sample with our method, and comparing with an already existing, manually created merger catalogue in terms of colour-mass distribution and stellar mass function.