activity
20192023
most citedCosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST

123 citations · 244 across the 6 of their papers we have counts for

collaborators

6 papers

astro-ph.CO2023★ 1 cited

Modeling the Galaxy Distribution in Clusters using Halo Cores

Danila Korytov, Esteban Rangel, Lindsey Bleem +5

The galaxy distribution in dark matter-dominated halos is expected to approximately trace the details of the underlying dark matter substructure. In this paper we introduce halo `c…

astro-ph.CO2021★ 8 cited

DESC DC2 Data Release Note

LSST Dark Energy Science Collaboration, Bela Abolfathi, Robert Armstrong +54

In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created…

astro-ph.IM2020★ 85 cited

The LSST DESC DC2 Simulated Sky Survey

LSST Dark Energy Science Collaboration, Bela Abolfathi, David Alonso +77

We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and…

astro-ph.CO2019★ 123 cited

CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST

Danila Korytov, Andrew Hearin, Eve Kovacs +27

This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the…

astro-ph.CO2019★ 2 cited

Lightcone Construction for HACC Cosmological Simulations with LANTERN

Joseph Hollowed

This document describes the LANTERN (Lightcone generAtion via sNapshoT intERpolatioN) code module, our current approach for generating lightcones from HACC [5] simulation products.…

astro-ph.CO2019★ 25 cited

HACC Cosmological Simulations: First Data Release

Katrin Heitmann, Thomas D. Uram, Hal Finkel +10

We describe the first major public data release from cosmological simulations carried out with Argonne's HACC code. This initial release covers a range of datasets from large gravi…