How Do Observational Astronomers Learn to Inspect Imaging Data
Hugo Walsh, Christopher Fluke, Sara Webb, and Lisa Wise

TL;DR
This study investigates how observational astronomers learn visual inspection skills amid increasing automation, revealing varied training methods and the emergence of expertise without formal training, to inform future educational approaches.
Contribution
It provides the first comprehensive survey of astronomers' training in visual inspection, highlighting informal learning and the phenomenon of expertise without formal instruction.
Findings
Majority received some form of training in visual inspection
Formal training is less common than informal training
Some experts train others without formal background
Abstract
Astronomy is entering an unprecedented era of data collection. Upcoming large surveys will gather more data than ever before, generated at rates requiring real-time decision making. Looking ahead, it is inevitable that astronomers will need to rely more heavily on automated processes. Indeed, some instances have already arisen wherein the majority of the inspection process is automated. Visual discovery, performed traditionally by humans, is one key area where automation is now being integrated rapidly. Visual discovery comprises two aspects: (1) visual inspection, the skill associated with examining an image to identify areas or objects of interest; and (2) visual interpretation, the knowledge associated with the classification of the objects or features. Both skills and knowledge are vital for humans to perform visual discovery, however, there appears to have been limited…
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