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We present an application of a particular machine-learning method (BoostedDecision Trees, BDTs using AdaBoost) to separate stars and galaxies inphotometric images using their catalog characteristics. BDTs are a wellestablished machine learning technique used for classification purposes. Theyhave been widely used specially in the field of particle and astroparticlephysics, and we use them here in an optical astronomy application. Thisalgorithm is able to improve from simple thresholding cuts on standardseparation variables that may be affected by local effects such as blending,badly calculated background levels or which do not include information in otherbands. The improvements are shown using the Sloan Digital Sky Survey DataRelease 9, with respect to the type photometric classifier. We obtain animprovement in the impurity of the galaxy sample of a factor 2-4 for thisparticular dataset, adjusting for the same efficiency of the selection. Anothermain goal of this study is to verify the effects that different input vectorsand training sets have on the classification performance, the results being ofwider use to other machine learning techniques.

Effect of training characteristics on object classification: an application using Boosted Decision Trees