SIMBAD references

We develop a robust machine learning classifier model utilizing the eXtreme-Gradient Boosting (XGB) algorithm for improved classification of Galactic Wolf-Rayet (WR) stars based on IR colors and positional attributes. For our study, we choose an extensive data set of 6555 stellar objects (from 2MASS and AllWISE data releases) lying in the Milky Way (MW) with available photometric magnitudes of different types, including WR stars. Our XGB classifier model can accurately (with an 86% detection rate) identify a sufficient number of WR stars against a large sample of non-WR sources. The XGB model outperforms other ensemble classifier models, such as Random Forest. Also, using the XGB algorithm, we develop a WR subtype classifier model that can differentiate the WR subtypes from the non-WR sources with a high model accuracy (>60%). Further, we apply both XGB-based models to a selection of 6457 stellar objects with unknown object types, detecting 58 new WR star candidates and predicting subtypes for 10 of them. The identified WR sources are mainly located in the local spiral arm of the MW and mostly lie in the solar neighborhood.