209 citations · 641 across the 9 of their papers we have counts for
9 papers
The Evolution of AGN Activity in Brightest Cluster Galaxies
T. Somboonpanyakul, M. McDonald, A. Noble +73
We present the results of an analysis of Wide-field Infrared Survey Explorer (WISE) observations on the full 2500 deg^2 South Pole Telescope (SPT)-SZ cluster sample. We describe a…
Dark Energy Survey Year 3 Results: A 2.7% measurement of Baryon Acoustic Oscillation distance scale at redshift 0.835
DES Collaboration, T. M. C. Abbott, M. Aguena +108
We present angular diameter measurements obtained by measuring the position of Baryon Acoustic Oscillations (BAO) in an optimised sample of galaxies from the first three years of D…
Understanding the extreme luminosity of DES14X2fna
M. Grayling, C. P. Gutiérrez, M. Sullivan +66
We present DES14X2fna, a high-luminosity, fast-declining type IIb supernova (SN IIb) at redshift , detected by the Dark Energy Survey (DES). DES14X2fna is an unusual memb…
Dark Energy Survey Year 3 Results: Clustering Redshifts -- Calibration of the Weak Lensing Source Redshift Distributions with redMaGiC and BOSS/eBOSS
M. Gatti, G. Giannini, G. M. Bernstein +94
We present the calibration of the Dark Energy Survey Year 3 (DES Y3) weak lensing source galaxy redshift distributions from clustering measurements. In particular, we cross-…
Dark Energy Survey Year 3 Results: Measuring the Survey Transfer Function with Balrog
S. Everett, B. Yanny, N. Kuropatkin +97
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) data…
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…