Density-based outlier scoring on Kepler data

Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly, truly novel phenomena that exhibit as-of-yet unobserved behaviours. In this work, we present an outlier scoring methodology to identify and characterize the most promising unusual sources to facilitate discoveries of such anomalous data.

We have developed a data mining method based on k-nearest neighbour distance in feature space to efficiently identify the most anomalous light curves. We test variations of this method including using principal components of the feature space, removing select features, the effect of the choice of k, and scoring to subset samples. We evaluate the performance of our scoring on known object classes and find that our scoring consistently scores rare (<1000) object classes higher than common classes.