An all-sky Support Vector Machine selection of WISE YSO Candidates

Abstract: We explored the AllWISE catalogue of the Wide-field Infrared Survey Explorer mission and identified Young Stellar Object candidates. Reliable 2MASS and WISE photometric data combined with Planck dust opacity values were used to build our dataset and to find the best classification scheme. A sophisticated statistical method, the Support Vector Machine (SVM) is used to analyse the multi-dimensional data space and to remove source types identified as contaminants (extragalactic sources, main sequence stars, evolved stars and sources related to the interstellar medium). Objects listed in the SIMBAD database are used to identify the already known sources and to train our method. A new all-sky selection of 133,980 Class I/II YSO candidates is presented. The estimated contamination was found to be well below 1% based on comparison with our SIMBAD training set. We also compare our results to that of existing methods and catalogues. The SVM selection process successfully identified >90% of the Class I/II YSOs based on comparison with photometric and spectroscopic YSO catalogues. Our conclusion is that by using the SVM, our classification is able to identify more known YSOs of the training sample than other methods based on colour-colour and magnitude-colour selection. The distribution of the YSO candidates well correlates with that of the Planck Galactic Cold Clumps in the Taurus--Auriga--Perseus--California region.