NEMESIS 通用年轻恒星天体目录——I. 基于深度学习方法的监督分类
The NEMESIS general YSO catalogue - I. Supervised classification with deep learning methods
- Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Hungarian Research Network (HUN-REN)（孔科利天文台，地球科学研究中心，匈牙利研究网络）
- CSFK, MTA Centre of Excellence（CSFK，马塔卓越中心）
- Department of Experimental Physics, Institute of Physics, University of Szeged（塞格德大学物理学院实验物理系）
- Université de Genève, Department of Astronomy（日内瓦大学天文学系）
- Université de Genève, Department of Basic Neuroscience（日内瓦大学基础神经科学系）
- Universität Wien, Institut für Astrophysik（维也纳大学天体物理学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
利用多波段数据构建图像表示，训练深度学习集成分类器，以高精度识别年轻恒星天体，并生成包含274,408个高可靠性YSO的NEMESIS通用目录。
AI中文摘要：
在现代大规模巡天中，年轻恒星天体（YSO）主要通过其光谱能量分布（SED）中的红外超量来识别。为此，YSO 的识别和分类主要基于特定颜色，导致方法依赖于特定波长。利用所有可用数据，我们可以通过将相同的识别方法应用于异质样本，构建一个统一分类的 YSO 目录，从而减少系统性差异，并实现对 YSO 种群和恒星形成更一致的研究。我们的目标是开发一种利用 VizieR 数据库中可用异质数据的方法，并提供一种能够高精度识别 YSO 的工具。我们还旨在创建最大的均匀 YSO 目录。我们使用 CDS VizieR 和 NASA/IPAC IRSA 收集大量不同类型天体的数据。收集的数据包括 SED、光变曲线和直接成像，并用于构建不同的图像表示。这些图像表示被用于训练深度学习算法，以区分 YSO 和所有其他类型的天体源。我们应用了来自 PyTorch 库的架构和自定义构建的卷积神经网络，创建了一个专门用于识别 YSO 的二分类器集成。所得到的方法能够自动识别 YSO 候选体，准确率高于 90%，而污染源的识别准确率高于 99%。我们将我们的分类器应用于众多 YSO 目录，以确认恒星的年轻性，并创建了有史以来最大的均匀识别的潜在 YSO 目录——NEMESIS 通用 YSO 目录包含 274,408 个唯一且高可靠性的 YSO。它们在颜色-星等图上的位置，以及它们相对于本地银河系结构的分布，也被用于检验其年轻性质。
英文摘要：
In modern large-scale surveys, young stellar objects (YSOs) are mostly identified by the infrared excess in their spectral energy distributions (SEDs). To this end, YSO identification and classification were mostly based on certain colours, resulting in wavelength-specific methods. Using all available data allows us to construct a uniformly classified YSO catalogue by applying the same identification method to heterogeneous samples, reducing systematic differences and enabling more consistent studies of YSO populations and star formation. Our goal is to develop a method that takes advantage of the heterogeneous data available in the VizieR database and to provide a tool that can be used to identify YSOs with high accuracy. We also aim to create the largest homogeneous catalogue of YSOs. We use the CDS VizieR and NASA/IPAC IRSA to collect data for a large number of objects of various types. The data collected included SEDs, light curves, and direct imaging and were used to build different image representations. These were used to teach deep learning algorithms to differentiate between YSOs and all other types of sources. Architectures from the PyTorch library and custom-built convolutional neural networks were applied to create an ensemble of binary classifiers specialised at identifying YSOs. The resulting method is able to automatically identify YSO candidates with an accuracy higher than 90\% while contamination is identified with an accuracy higher than 99\%. We applied our classifier to numerous YSO catalogues to confirm the youth of the stars and to create the largest catalogue of homogeneously identified potential YSOs ever -- the NEMESIS General YSO catalogue contains 274\,408 unique, high reliability YSOs. Their positions on the colour-magnitude diagrams, and their distribution relative to the local Galactic structures, were also used to test their young nature.
