标题： 半监督学习在透镜化类星体探测中的应用 显示英文标题

标题： Semi-Supervised Learning for Lensed Quasar Detection

摘要： 引力透镜化类星体是天文学许多领域的重要研究对象，为我们探索中远宇宙提供了独特视角。然而，尽管大型合作项目付出了巨大努力，寻找引力透镜化类星体依然困难重重。这些挑战使得已确认的引力透镜化类星体目录数量仅限于数百个，而理论预测它们的数量应该多得多。 我们训练了机器学习分类器来发现引力透镜化类星体候选者。通过使用半监督学习技术，我们利用大量潜在候选者的未标记训练数据以及少量已知对象，极大地提高了模型性能。 我们展示了两个最成功的模型：（1）一个变分自编码器，它在数百万个类星体上进行训练以降低图像维度，然后将其输入到梯度提升分类器中，从而能够做出准确预测；（2）一个卷积神经网络，通过虚拟对抗性训练，在标记和未标记的数据混合上进行训练。 这两个模型都能够产生高质量的候选者，这一点从我们发现了GRALJ140833.73+042229.98就可以得到证明。我们分类器的成功，尤其是仅基于图像的情况下，尤其令人兴奋，因为它可以与现有的分类器结合，后者使用除图像以外的其他数据，从而改善两种模型的分类并发现更多引力透镜化类星体。 显示英文摘要

摘要： Lensed quasars are key to many areas of study in astronomy, offering a unique probe into the intermediate and far universe. However, finding lensed quasars has proved difficult despite significant efforts from large collaborations. These challenges have limited catalogues of confirmed lensed quasars to the hundreds, despite theoretical predictions that they should be many times more numerous. We train machine learning classifiers to discover lensed quasar candidates. By using semi-supervised learning techniques we leverage the large number of potential candidates as unlabelled training data alongside the small number of known objects, greatly improving model performance. We present our two most successful models: (1) a variational autoencoder trained on millions of quasars to reduce the dimensionality of images for input to a gradient boosting classifier that can make accurate predictions and (2) a convolutional neural network trained on a mix of labelled and unlabelled data via virtual adversarial training. These models are both capable of producing high-quality candidates, as evidenced by our discovery of GRALJ140833.73+042229.98. The success of our classifier, which uses only images, is particularly exciting as it can be combined with existing classifiers, which use other data than images, to improve the classifications of both models and discover more lensed quasars.