Using transfer learning to detect galaxy mergers
Using transfer learning to detect galaxy mergers
复制标题
使用迁移学习来检测星系合并
DOI：
10.1093/mnras/sty1398
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. D. Turp
中科院分区：
文献类型：
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作者：
Sandro Ackermann;K. Schawinski;Ce Zhang;Anna K. Weigel;M. D. Turp
我们研究了使用深度卷积神经网络（deep cnn）对星系合并进行自动视觉检测。此外，我们研究了迁移学习与cnn的结合使用，通过对首先在日常物体图片上训练的网络进行再训练。我们验证了迁移学习对于提高小训练集的分类性能有用的假设。这将使迁移学习有助于在天文成像数据集中寻找稀有物体。我们发现这些深度学习方法的表现明显优于当前基于非参数系统（如CAS和GM）的最先进的合并检测方法。我们的方法是端到端的，对图像噪声和失真具有鲁棒性；它可以直接应用，无需图像预处理。我们还发现迁移学习在某些情况下可以作为正则化器，导致更好的整体分类精度（$p = 0.02$）。在我们的完整训练集上迁移学习导致错误率从0.038 $\pm$ 1下降到0.032 $\pm$ 1，相对提高了15%。最后，我们用我们的方法创建了一个合并样本，并在颜色质量分布和恒星质量函数方面与已经存在的手动创建的合并目录进行了比较，从而进行了基本的完整性检查。
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.