Dark Energy Survey Year 6 Results: Synthetic-source Injection Across the Full Survey Using Balrog
Abstract
Synthetic source injection (SSI), the insertion of sources into pixel-level on-sky images, is a powerful method for characterizing object detection and measurement in wide-field, astronomical imaging surveys. Within the Dark Energy Survey (DES), SSI plays a critical role in characterizing all necessary algorithms used in converting images to catalogs, and in deriving quantities needed for the cosmology analysis, such as object detection rates, galaxy redshift estimation, galaxy magnification, star-galaxy classification, and photometric performance. We present here a source injection catalog of million injections spanning the entire 5000 deg DES footprint, generated using the Balrog SSI pipeline. Through this sample, we demonstrate that the DES Year 6 (Y6) image processing pipeline provides accurate estimates of the object properties, for both galaxies and stars, at the percent-level, and we highlight specific regimes where the accuracy is reduced. We then show the consistency between SSI and data catalogs, for all galaxy samples developed within the weak lensing and galaxy clustering analyses of DES Y6. The consistency between the two catalogs also extends to their correlations with survey observing properties (seeing, airmass, depth, extinction, etc.). Finally, we highlight a number of applications of this catalog to the DES Y6 cosmology analysis. This dataset is the largest SSI catalog produced at this fidelity and will serve as a key testing ground for exploring the utility of SSI catalogs in upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.
Cite
@article{arxiv.2501.05683,
title = {Dark Energy Survey Year 6 Results: Synthetic-source Injection Across the Full Survey Using Balrog},
author = {D. Anbajagane and M. Tabbutt and J. Beas-Gonzalez and B. Yanny and S. Everett and M. R. Becker and M. Yamamoto and E. Legnani and J. De Vicente and K. Bechtol and J. Elvin-Poole and G. M. Bernstein and A. Choi and M. Gatti and G. Giannini and R. A. Gruendl and M. Jarvis and S. Lee and J. Mena-Fernández and A. Porredon and M. Rodriguez-Monroy and E. Rozo and E. S. Rykoff and T. Schutt and E. Sheldon and M. A. Troxel and N. Weaverdyck and V. Wetzell and M. Aguena and A. Alarcon and S. Allam and A. Amon and F. Andrade-Oliveira and J. Blazek and D. Brooks and A. Carnero Rosell and J. Carretero and C. Chang and M. Crocce and L. N. da Costa and M. E. S. Pereira and T. M. Davis and S. Desai and H. T. Diehl and S. Dodelson and P. Doel and A. Drlica-Wagner and A. Ferté and J. Frieman and J. García-Bellido and E. Gaztanaga and D. Gruen and G. Gutierrez and W. G. Hartley and K. Herner and S. R. Hinton and D. L. Hollowood and K. Honscheid and D. Huterer and D. J. James and E. Krause and K. Kuehn and O. Lahav and J. L. Marshall and R. Miquel and J. Muir and J. Myles and A. Pieres and A. A. Plazas Malagón and J. Prat and M. Raveri and S. Samuroff and E. Sanchez and D. Sanchez Cid and I. Sevilla-Noarbe and M. Smith and E. Suchyta and G. Tarle and D. L. Tucker and A. R. Walker and P. Wiseman and Y. Zhang},
journal= {arXiv preprint arXiv:2501.05683},
year = {2025}
}
Comments
v2: accepted to OJA