NACluster: A Non-supervised Clustering Algorithm for Matching Multi Catalogues

Astronomy surveys use powerful instruments to browse the sky and identify objects of interest within the surveyed region. Sky objects are individually characterized with spatial coordinates, identifying their position in the sky, in addition to other descriptive attributes. Composing an integrated view of the sky based on catalogues produced by different surveys faces a hard problem of matching objects that have been captured in various catalogues. Due to variations on capturing instruments calibration, the sky position of a single sky object may vary from a catalog to the other. Moreover, in particular dense regions of the sky this problem is exacerbated by a huge number of candidate matches for each given object. Traditional approaches for dealing with this problem use a threshold distance of "&b.epsi;" to reduce the number of matching candidates. Additionally, they adopt a pairwise approach for matching "n" catalogues inferring transitivity among matches, which not always hold. In this paper, we present NACluster a non-supervised clustering algorithm for dealing with sky object matching in multiple catalogues. NACluster matching strategy extends the traditional k-means clustering algorithm by relaxing the number k of cluster (i.e. matched sky objects). We experiment NACluster with real and synthetic catalogues and show that the results present better accuracy than state of the art solutions.