Crowdsourcing quality control for Dark Energy Survey images

TL;DR

This paper presents a crowdsourcing web application for image quality control in the Dark Energy Survey, enabling users to identify and report issues in astronomical images, thereby improving data quality efficiently.

Contribution

The paper introduces a novel crowdsourcing tool for astronomical image quality control, including user engagement strategies and lessons learned from deployment with over 100 users.

Findings

User reports helped rapidly correct subtle image artifacts

The platform engaged both professional and amateur astronomers

Open-source code and online demo are available for community use

Abstract

We have developed a crowdsourcing web application for image quality control employed by the Dark Energy Survey. Dubbed the "DES exposure checker", it renders science-grade images directly to a web browser and allows users to mark problematic features from a set of predefined classes. Users can also generate custom labels and thus help identify previously unknown problem classes. User reports are fed back to hardware and software experts to help mitigate and eliminate recognized issues. We report on the implementation of the application and our experience with its over 100 users, the majority of which are professional or prospective astronomers but not data management experts. We discuss aspects of user training and engagement, and demonstrate how problem reports have been pivotal to rapidly correct artifacts which would likely have been too subtle or infrequent to be recognized otherwise. We conclude with a number of important lessons learned, suggest possible improvements, and recommend this collective exploratory approach for future astronomical surveys or other extensive data sets with a sufficiently large user base. We also release open-source code of the web application and host an online demo version at http://des-exp-checker.pmelchior.net