Towards automatic classification of all WISE sources
Agnieszka Kurcz, Maciej Bilicki, Aleksandra Solarz, Magdalena Krupa,, Agnieszka Pollo, Katarzyna Ma{\l}ek

TL;DR
This paper develops an automatic classification method using support vector machines to categorize WISE sources into stars, galaxies, and quasars, achieving high accuracy and creating comprehensive all-sky catalogues based solely on WISE data.
Contribution
It introduces a novel SVM-based classification approach tailored for WISE data, utilizing only parameters available from WISE, and demonstrates its effectiveness in large-scale source categorization.
Findings
Achieved ~95% completeness for stars and galaxies at bright magnitudes
Maintained ~95% purity for quasars across magnitudes
Generated reliable all-sky star and galaxy catalogues from WISE data
Abstract
The WISE satellite has detected hundreds of millions sources over the entire sky. Classifying them reliably is however a challenging task due to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Here we aim at obtaining comprehensive and reliable star, galaxy and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those reliably measured for a majority of sources. For the automatic classification we applied the support vector machines (SVM) algorithm, which requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset for that purpose. By calibrating the classifier on the test data drawn from SDSS, we first established that a…
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