论文标题
天体物理学的异常检测：无监督的深度与机器学习之间的比较
Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data
论文作者
论文摘要
科学的每个领域都在发现过程中发生了前所未有的变化，自一开始以来，天文学一直是该过渡的主要参与者。持续和未来的大型和复杂的多门会天空调查对强大而有效的自动化方法施加了广泛的利用，以对观察到的结构进行分类并检测和表征和表征特殊和意外的来源。我们通过应用异常检测的问题进行了两种不同的无监督的机器学习算法，对KIDS DR4数据进行了初步实验，该问题被认为是检测到特殊来源的潜在有前途的方法，这是一个分散的卷积自动编码器和一个无人监督的随机森林。前一种方法直接在图像上工作，被认为可以识别诸如相互作用星系和重力透镜之类的特殊物体。后来的是，在目录数据上工作，可以识别具有异常大小和颜色值的对象，这反过来又表明存在奇异性。
Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities.
