7 papers
The final WaZP galaxy cluster catalog of the Dark Energy Survey and comparison with SZE data
C. Benoist, M. Aguena, L. da Costa +43
In this work, we present and characterize the galaxy cluster catalog detected by the WaZP cluster finder, which is not based on red-sequence identification, on the full six years o…
Constraints on cosmology and baryonic feedback with joint analysis of Dark Energy Survey Year 3 lensing data and ACT DR6 thermal Sunyaev-Zel'dovich effect observations
S. Pandey, J. C. Hill, A. Alarcon +137
We present a joint analysis of weak gravitational lensing (shear) data obtained from the first three years of observations by the Dark Energy Survey and thermal Sunyaev-Zel'dovich…
Dark Energy Survey Year 3 results: CDM cosmology from simulation-based inference with persistent homology on the sphere
J. Prat, M. Gatti, C. Doux +94
We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how featu…
Cosmology with second and third-order shear statistics for the Dark Energy Survey: Methods and simulated analysis
R. C. H. Gomes, S. Sugiyama, B. Jain +56
We present a new pipeline designed for the robust inference of cosmological parameters using both second- and third-order shear statistics. We build a theoretical model for rapid e…
Selection Function of Clusters in Dark Energy Survey Year 3 Data from Cross-Matching with South Pole Telescope Detections
S. Grandis, M. Costanzi, J. J. Mohr +52
Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. Yet modeling the selection function of such samples is compl…
Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps
J. Gonzalez, P. Holloway, T. Collett +70
We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learnin…