# Using transfer learning to detect galaxy mergers

arXiv: https://arxiv.org/abs/1805.10289 · HTML: https://mlsanity.com/paper/1805.10289/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 11 of 11)

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- [Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces](https://mlsanity.com/paper/2305.00002/) (2023)
- [Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide Survey](https://mlsanity.com/paper/2501.15739/) (2025)
- [CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices](https://mlsanity.com/paper/2510.25323/) (2025)
- [A Deep Neural Network -- Mechanistic Hybrid Model to Predict Pharmacokinetics in Rat](https://mlsanity.com/paper/2310.09167/) (2023)

## Abstract (arXiv)

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 GM20. 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 ± 1 down to 0.032 ± 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.