Abstract: Identifying unusual or anomalous observations can lead to new discoveries, expose problems in pipeline processing, overturn existing theories, and stimulate new hypotheses. Our objective is to build software tools and methods for the creation of catalogs of astrophysical anomalies. Our process isolates and organizes anomalies in large astronomical survey data sets, and provides explanations to help distinguish between scientifically-interesting astrophysical and unphysical anomalies such as data artifacts and modeling errors. We are prototyping this work on Dark Energy Survey (DES) catalog data, with plans to extend to other survey data. The publication of these types of anomaly catalogs addresses an urgent need to plumb the depths of astronomical archives for new discoveries. The associated software framework provides the community with robust methods for finding, organizing and explaining anomalous observations.
