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The Dark Energy Survey Supernova Program: Modelling selection efficiency and observed core collapse supernova contamination
M. Vincenzi, M. Sullivan, O. Graur +79
The analysis of current and future cosmological surveys of type Ia supernovae (SNe Ia) at high-redshift depends on the accurate photometric classification of the SN events detected…
Dark Energy Survey Year 3 Results: Deep Field Optical + Near-Infrared Images and Catalogue
W. G. Hartley, A. Choi, A. Amon +115
We describe the Dark Energy Survey (DES) Deep Fields, a set of images and associated multi-wavelength catalogue () built from Dark Energy Camera (DECam) and Visible and…
Assessing tension metrics with Dark Energy Survey and Planck data
P. Lemos, M. Raveri, A. Campos +103
Quantifying tensions -- inconsistencies amongst measurements of cosmological parameters by different experiments -- has emerged as a crucial part of modern cosmological data analys…
DES Y3 results: Blending shear and redshift biases in image simulations
N. MacCrann, M. R. Becker, J. McCullough +93
As the statistical power of galaxy weak lensing reaches percent level precision, large, realistic and robust simulations are required to calibrate observational systematics, especi…
A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
S. Mucesh, W. G. Hartley, A. Palmese +72
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorit…
Identifying RR Lyrae Variable Stars in Six Years of the Dark Energy Survey
K. M. Stringer, A. Drlica-Wagner, L. Macri +67
We present a search for RR Lyrae stars using the full six-year data set from the Dark Energy Survey (DES) covering ~5,000 sq. deg. of the southern sky. Using a multi-stage multi-va…