Andrew C. Becker, Dominik J. Bomans, K. Weis

• Obtaining a complete census of massive, evolved stars in a galaxy would be a key ingredient for testing stellar evolution models. However, as the evolution of stars is also strongly dependent on their metallicity, it is inevitable to have this kind of data for a variety of galaxies with different metallicities. Between 2009 and 2011, we conducted the Magellanic Clouds Massive Stars and Feedback Survey (MSCF); a spatially complete, multi-epoch, broad- and narrow-band optical imaging survey of the Large and Small Magellanic Clouds. With the inclusion of shallow images, we are able to give a complete photometric catalog of stars between B ≈ 18 and B ≈ 19 mag. These observations were augmented with additional photometric data of similar spatial res- olution from UV to IR (e.g. from GALEX, 2MASS and Spitzer) in order to sample a large portion of the spectral energy distribution of the brightest stars (B < 16 mag) in the Magel- lanic Clouds. Using these data, were are able to train a machine learning algorithm that gives us a goodObtaining a complete census of massive, evolved stars in a galaxy would be a key ingredient for testing stellar evolution models. However, as the evolution of stars is also strongly dependent on their metallicity, it is inevitable to have this kind of data for a variety of galaxies with different metallicities. Between 2009 and 2011, we conducted the Magellanic Clouds Massive Stars and Feedback Survey (MSCF); a spatially complete, multi-epoch, broad- and narrow-band optical imaging survey of the Large and Small Magellanic Clouds. With the inclusion of shallow images, we are able to give a complete photometric catalog of stars between B ≈ 18 and B ≈ 19 mag. These observations were augmented with additional photometric data of similar spatial res- olution from UV to IR (e.g. from GALEX, 2MASS and Spitzer) in order to sample a large portion of the spectral energy distribution of the brightest stars (B < 16 mag) in the Magel- lanic Clouds. Using these data, were are able to train a machine learning algorithm that gives us a good estimate of the spectral type of tens of thousands of stars. This method can be applied to the search for Wolf-Rayet-Stars to obtain a sample of candi- dates for follow-up observations. As this approach can, in principle, be adopted for any resolved galaxy as long as sufficient photometric data is available, it can form an effective alternative method to the classical strategies (e.g. He II filter imaging).…