A Hitchhiker's Guide to Anomaly Detection（英语：A Hitchhiker's Guide to Anomaly Detection）
A Hitchhiker's Guide to Anomaly Detection with Astronomaly
摘要：
下一代望远镜，如SKA和鲁宾天文台将产生巨大的数据集，需要自动异常检测，以实现科学发现。在这里，我们提出了一个概述和友好的用户指南，天文学框架的主动异常检测天文数据。Astronomaly使用主动学习将机器学习的原始处理能力与人类用户的直觉和经验相结合，从而实现对有趣异常的个性化建议。它使用Python后端来执行数据处理，特征提取和机器学习以检测异常对象;以及JavaScript前端，允许与数据进行交互，标记有趣的异常和主动学习。Astronomaly被设计成模块化的，可扩展的，几乎可以在任何类型的天文数据上运行。在本文中，我们详细介绍了Astronomaly代码的结构，并提供了基本使用指南。
英文摘要：
The next generation of telescopes such as the SKA and the Rubin Observatory will produce enormous data sets, requiring automated anomaly detection to enable scientific discovery. Here, we present an overview and friendly user guide to the Astronomaly framework for active anomaly detection in astronomical data. Astronomaly uses active learning to combine the raw processing power of machine learning with the intuition and experience of a human user, enabling personalised recommendations of interesting anomalies. It makes use of a Python backend to perform data processing, feature extraction and machine learning to detect anomalous objects; and a JavaScript frontend to allow interaction with the data, labelling of interesting anomalous and active learning. Astronomaly is designed to be modular, extendable and run on almost any type of astronomical data. In this paper, we detail the structure of the Astronomaly code and provide guidelines for basic usage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
