论文标题
用于改进星系合并的跨域研究的域适应技术
Domain adaptation techniques for improved cross-domain study of galaxy mergers
论文作者
论文摘要
在天文学中，通常对神经网络进行模拟数据训练，以便将其应用于实际观察。不幸的是，简单地在一个域中的图像上训练深层神经网络并不能保证来自不同域的新图像的令人满意的性能。共享跨域知识的能力是现代深区适应技术的主要优势。在这里，我们证明了两种技术的使用 - 最大平均差异（MMD）和针对域对抗神经网络（DANN）的对抗训练 - 用于分类来自Illustris -1模拟的遥远的星系合并，其中两个域仅由于纳入观察噪声而出现的两个域才出现。我们展示了与传统的机器学习算法相比，MMD或对抗训练的添加如何极大地提高了分类器在目标域上的性能，从而证明了它们在天文学中使用的巨大希望。
In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques. Here we demonstrate the use of two techniques - Maximum Mean Discrepancy (MMD) and adversarial training with Domain Adversarial Neural Networks (DANN) - for the classification of distant galaxy mergers from the Illustris-1 simulation, where the two domains presented differ only due to inclusion of observational noise. We show how the addition of either MMD or adversarial training greatly improves the performance of the classifier on the target domain when compared to conventional machine learning algorithms, thereby demonstrating great promise for their use in astronomy.
