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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…
Dark Energy Survey Year 3 Results: Covariance Modelling and its Impact on Parameter Estimation and Quality of Fit
O. Friedrich, F. Andrade-Oliveira, H. Camacho +109
We describe and test the fiducial covariance matrix model for the combined 2-point function analysis of the Dark Energy Survey Year 3 (DES-Y3) dataset. Using a variety of new ansat…
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…
Dark Energy Survey Year 3 Results: Redshift Calibration of the Weak Lensing Source Galaxies
J. Myles, A. Alarcon, A. Amon +105
Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey is an essential component to mapping the matter den…
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…