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Classifying catalog objects as stars, galaxies, or AGN is a crucial part ofany statistical study of galaxies. We describe our pipeline for binary(star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN)classification developed for the very deep CLAUDS+HSC-SSP $u^*grizy$ dataset.Our method uses the XGBoost implementation of Gradient Boosted Trees (GBT) totrain ensembles of models which take photometry, colours, maximum surfacebrightnesses, and effective radii from all available bands as input, and outputthe probability that an object belongs to each of the classes underconsideration. At $i_{AB}<25$ our binary star/galaxy model has AUC=0.9974 andat the threshold that maximizes our sample's weighted F1 score, selects asample of galaxies with 99.7% purity and 99.8% completeness. We test themodel's ability to generalize to objects fainter than those seen duringtraining and find that extrapolation of ~1-2 magnitudes is reasonable for mostapplications provided that the galaxies in the training sample arerepresentative of the range of redshifts and colours of the galaxies in thetarget sample. We also perform an exploratory analysis of the method's abilityto identify AGN using a small x-ray selected sample and find that it holdspromise for classifying type I AGN, although it performs less well for type IIAGN. Our results demonstrate that GBTs provide a flexible, robust and efficientmethod for performing classification of catalog objects in large astronomicalimaging surveys.

Classifying Stars, Galaxies and AGN in CLAUDS+HSC-SSP Using Gradient Boosted Decision Trees