661 citations · 4.4k across the 46 of their papers we have counts for
47 papers
Weak lensing, Big Magnification: Recovering hidden lensing information from galaxy positions
Alex Alarcon, Carles Sánchez
Galaxy clustering and weak gravitational lensing are powerful cosmological probes, traditionally combined through galaxy clustering and galaxy-galaxy lensing in a pt anal…
Dark Energy Survey Year 3 results: Galaxy-halo connection from galaxy-galaxy lensing
G. Zacharegkas, C. Chang, J. Prat +114
Galaxy-galaxy lensing is a powerful probe of the connection between galaxies and their host dark matter halos, which is important both for galaxy evolution and cosmology. We extend…
Dark Energy Survey Year 3 results: cosmology from combined galaxy clustering and lensing -- validation on cosmological simulations
J. DeRose, R. H. Wechsler, M. R. Becker +114
We present a validation of the Dark Energy Survey Year 3 (DES Y3) -point analysis choices by testing them on Buzzard v2.0, a new suite of cosmological simulations that is…
The mass and galaxy distribution around SZ-selected clusters
T. Shin, B. Jain, S. Adhikari +136
We present measurements of the radial profiles of the mass and galaxy number density around Sunyaev-Zel'dovich-selected clusters using both weak lensing and galaxy counts. The clus…
Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and galaxy-galaxy lensing using the MagLim lens sample
A. Porredon, M. Crocce, J. Elvin-Poole +141
Two of the most sensitive probes of the large scale structure of the universe are the clustering of galaxies and the tangential shear of background galaxy shapes produced by those…
Dark Energy Survey Year 3 Results: Constraints on cosmological parameters and galaxy bias models from galaxy clustering and galaxy-galaxy lensing using the redMaGiC sample
S. Pandey, E. Krause, J. DeRose +138
We constrain cosmological and galaxy-bias parameters using the combination of galaxy clustering and galaxy-galaxy lensing measurements from the Dark Energy Survey Year-3 data. We d…