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The next generation of telescopes such as the SKA and the Rubin Observatorywill produce enormous data sets, requiring automated anomaly detection toenable scientific discovery. Here, we present an overview and friendly userguide to the Astronomaly framework for active anomaly detection in astronomicaldata. Astronomaly uses active learning to combine the raw processing power ofmachine learning with the intuition and experience of a human user, enablingpersonalised recommendations of interesting anomalies. It makes use of a Pythonbackend to perform data processing, feature extraction and machine learning todetect anomalous objects; and a JavaScript frontend to allow interaction withthe data, labelling of interesting anomalous and active learning. Astronomalyis designed to be modular, extendable and run on almost any type ofastronomical data. In this paper, we detail the structure of the Astronomalycode and provide guidelines for basic usage.

A Hitchhiker's Guide to Anomaly Detection with Astronomaly