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

TL;DR

The paper introduces Balrog, a simulation framework for calibrating and diagnosing the Dark Energy Survey Year 3 data, effectively capturing measurement biases and systematics for improved cosmological analyses.

Contribution

Balrog provides a novel, realistic injection-based method to sample the survey transfer function, enhancing calibration of photometric redshifts and magnification biases in DES Y3 data.

Findings

Balrog injections closely match real data color distributions.

Photometric calibration in Y3 is accurate within 1-8 millimagnitudes.

Detected magnitude biases linked to object size overestimates in certain cases.

Abstract

We describe an updated calibration and diagnostic framework, Balrog, used to directly sample the selection and photometric biases of the Dark Energy Survey's (DES) Year 3 (Y3) dataset. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with traditional generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful 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. The recovered Balrog injections are shown to closely match the photometric property distributions of the Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ~1-8 millimagnitudes, but for a small subset of objects we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. We discuss approaches to extend the current methodology to capture more aspects of the transfer function and reach full coverage of the survey footprint for future analyses.