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We describe an updated calibration and diagnostic framework, Balrog, used todirectly sample the selection and photometric biases of the Dark EnergySurvey's (DES) Year 3 (Y3) dataset. We systematically inject onto thesingle-epoch images of a random 20% subset of the DES footprint an ensemble ofnearly 30 million realistic galaxy models derived from DES Deep Fieldobservations. These augmented images are analyzed in parallel with the originaldata to automatically inherit measurement systematics that are often toodifficult to capture with traditional generative models. The resulting objectcatalog is a Monte Carlo sampling of the DES transfer function and is used as apowerful diagnostic and calibration tool for a variety of DES Y3 science,particularly for the calibration of the photometric redshifts of distant"source" galaxies and magnification biases of nearer "lens" galaxies. Therecovered Balrog injections are shown to closely match the photometric propertydistributions of the Y3 GOLD catalog, particularly in color, and capture thenumber density fluctuations from observing conditions of the real data within1% for a typical galaxy sample. We find that Y3 colors are extremely wellcalibrated, typically within ~1-8 millimagnitudes, but for a small subset ofobjects we detect significant magnitude biases correlated with largeoverestimates of the injected object size due to proximity effects andblending. We discuss approaches to extend the current methodology to capturemore aspects of the transfer function and reach full coverage of the surveyfootprint for future analyses.

Dark Energy Survey Year 3 Results: Measuring the Survey Transfer Function with Balrog