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1 November 2001 Data mining for multiwavelength cross-referencing
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Abstract
In this paper, we deal with FOCA ultraviolet data and their cross-referencing with the DPOSS optical catalog, through data mining techniques. While traditional cross-referencing consists in correcting catalog coordinates in order to seek nearest candidate, non-optical surveys tend to have lower resolutions and more coordinates uncertainties. Then, it seemed to be a loss not to use more light sources parameters obtained through image processing pipelines. A data mining approach based on decision trees (machine learning algorithms), we processed different FOCA/DPOSS sources pairs that we could suppose being the same stellar entity, and some other pairs, obviously too distant to match. Trees use every existing ultraviolet/optical parameter present on catalog, excluding only coordinates. The resulting trees allows a classification of any FOCA/DPOSS pair, giving a probability for the pair to match, i.e. come from the same source. The originality of this method is the use of non-position parameters, that can be used for cross-referencing various catalogs in different wavelength without the need to homogenize coordinates systems. Such methods could be tools for working on upcoming multi-wavelength catalogs.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bruno Voisin and Jose Donas "Data mining for multiwavelength cross-referencing", Proc. SPIE 4477, Astronomical Data Analysis, (1 November 2001); https://doi.org/10.1117/12.447184
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