296 citations · 1.6k across the 28 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
Pushing automated morphological classifications to their limits with the Dark Energy Survey
J. Vega-Ferrero, H. Domínguez Sánchez, M. Bernardi +60
We present morphological classifications of 27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classif…
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…