标题： 领域自适应技术用于星系合并的跨领域研究 显示英文标题

标题： Domain adaptation techniques for improved cross-domain study of galaxy mergers

摘要： 在天文学中，神经网络通常在模拟数据上进行训练，以期应用于实际观测数据。 不幸的是，仅仅在一个领域图像上训练深度神经网络并不能保证在不同领域的新图像上表现出色。 共享跨领域知识的能力是现代深度领域自适应技术的主要优势。 在这里，我们展示了两种技术——最大均值差异（MMD）和使用领域对抗神经网络（DANN）的对抗训练——用于从Illustris-1模拟中对遥远星系合并进行分类，其中两个领域仅由于包含观测噪声而有所不同。 我们展示了与传统机器学习算法相比，添加MMD或对抗训练如何显著提高分类器在目标领域的性能，从而证明了它们在天文学中应用的巨大潜力。 显示英文摘要

摘要： 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.