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The Dark Energy Survey is able to collect image data of an extremely largenumber of extragalactic objects, and it can be reasonably assumed that manyunusual objects of high scientific interest are hidden inside these data. Dueto the extreme size of DES data, identifying these objects among many millionsof other celestial objects is a challenging task. The problem of outlierdetection is further magnified by the presence of noisy or saturated images.When the number of tested objects is extremely high, even a small rate of noiseor false positives leads to a very large number of false detections, making anautomatic system impractical. This study applies an automatic method forautomatic detection of outlier objects in the first data release of the DarkEnergy Survey. By using machine learning-based outlier detection, the algorithmis able to identify objects that are visually different from the majority ofthe other objects in the database. An important feature of the algorithm isthat it allows to control the false-positive rate, and therefore can be usedfor practical outlier detection. The algorithm does not provide perfectaccuracy in the detection of outlier objects, but it reduces the datasubstantially to allow practical outlier detection. For instance, the selectionof the top 250 objects after applying the algorithm to more than $2\cdot10^6$DES images provides a collection of uncommon galaxies. Such collection wouldhave been extremely time-consuming to compile by using manual inspection of thedata.

Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning