AI 中文总结
SNAD团队开发含专家在环主动学习算法的Coniferest库等工具，将其应用于ZTF数据发现超新星候选体及多种稀有天体，实现大数据时代天文学的高效科学发现。
AI 中文摘要
在天文学的宽场巡天与大数据时代，SNAD团队正利用现代数据集的潜力，通过机器学习（ML）发现新的、未预见的或稀有的天体物理天体与现象。SNAD流程基于以下假设构建：尽管自动ML算法在该任务中发挥关键作用，但只有当此类系统被设计为增强领域知识专家的影响力时，科学发现才会完全实现。我们的核心贡献包括开发Coniferest Python库，该库实现了两种带有“专家在环”的主动学习算法；创建SNAD瞬变源挖掘器，以促进特定类型瞬变源的搜索；还开发了SNAD Viewer，这是一个网络门户，提供兹威基瞬变设施（ZTF）数据发布中单个天体的集中视图，使潜在异常的分析更高效。最后，当将我们的方法应用于ZTF数据时，已产生超过100个新的超新星（SN）候选体，以及其他一些未编入目录的天体，如红矮星耀斑、超亮超新星、RS CVn型变星和年轻恒星天体。
英文摘要
In the era of wide-field surveys and big data in astronomy, the SNAD team is exploiting the potential of modern datasets for discovering new, unforeseen, or rare astrophysical objects and phenomena with machine learning (ML). The SNAD pipeline was built under the hypothesis that, although automatic ML algorithms have a crucial role to play in this task, the scientific discovery is only completely realized when such systems are designed to boost the impact of domain knowledge experts. Our key contributions include the development of the Coniferest Python library, which offers implementations of two active learning algorithms with an ``expert in loop'', and the creation of the SNAD Transient Miner, facilitating the search for specific types of transients. We have also developed the SNAD Viewer, a web portal that provides a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, making the analysis of potential anomalies more efficient. Finally, when applied to ZTF data, our approach has resulted in more than a hundred new supernova (SN) candidates, along with a few other non-catalogued objects, such as red dwarf flares, superluminous SNe, RS CVn type variables, and young stellar objects.
CommentsFrontier Research in Astrophysics - IV (FRAPWS2024), 9-14 September 2024, Mondello, Palermo, Italy
Journal refProceedings of Science, Volume 482, published on: October 07, 2025
