论文标题
搭便车指南与天文学的异常检测指南
A Hitchhiker's Guide to Anomaly Detection with Astronomaly
论文作者
论文摘要
下一代望远镜（例如SKA和鲁宾天文台）将产生巨大的数据集，需要自动化的异常检测才能实现科学发现。在这里，我们介绍了天文学框架的概述和友好的用户指南，用于天文数据中的主动异常检测。天文学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，从而实现了有趣的异常建议。它利用Python后端执行数据处理，功能提取和机器学习以检测异常对象。以及JavaScript前端，允许与数据相互作用，标记有趣的异常和主动学习。天文学设计为模块化，可扩展，并在几乎任何类型的天文数据上运行。在本文中，我们详细介绍了天文学法规的结构，并为基本使用提供了指南。
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.
