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Samuel W. Skillman

Researcher at University of Colorado Boulder

Publications -  46
Citations -  6029

Samuel W. Skillman is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Galaxy cluster & Intracluster medium. The author has an hindex of 24, co-authored 46 publications receiving 5418 citations. Previous affiliations of Samuel W. Skillman include United States Department of Energy & Stanford University.

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yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data

TL;DR: Yt, an open source, community-developed astrophysical analysis and visualization toolkit, is presented and its methods for reading, handling, and visualizing data, including projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation, and topologically connected isocontour identification are reported.
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A Multi-Code Analysis Toolkit for Astrophysical Simulation Data

TL;DR: In this paper is an open source, community-developed astrophysical analysis and visualization toolkit, which is oriented around physically relevant quantities rather than quantities native to astrophysical simulation codes, including Enzo's structure adaptive mesh refinement (AMR).
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Enzo: an adaptive mesh refinement code for astrophysics

TL;DR: Enzo as discussed by the authors uses block-structured adaptive mesh refinement to provide high spatial and temporal resolution for modeling astrophysical fluid flows, which can be run in one, two, and three dimensions, and supports a wide variety of physics, including hydrodynamics, ideal and non-ideal magnetohydrodynamic, N-body dynamics, primordial gas chemistry, optically thin radiative cooling of primordial and metal-enriched plasmas, and models for star formation and feedback in a cosmological context.
Journal ArticleDOI

Enzo: An Adaptive Mesh Refinement Code for Astrophysics

TL;DR: Enzo as mentioned in this paper uses block-structured adaptive mesh refinement to provide high spatial and temporal resolution for modeling astrophysical fluid flows, which can be run in 1, 2, and 3 dimensions, and supports a wide variety of physics, including hydrodynamics, ideal and non-ideal magnetohydrodynamic, N-body dynamics, primordial gas chemistry, optically-thin radiative cooling of primordial and metal-enriched plasmas, and models for star formation and feedback.

Enzo: An Adaptive Mesh Refinement Code for Astrophysics

TL;DR: Enzo as discussed by the authors uses block-structured adaptive mesh refinement to provide high spatial and temporal resolution for modeling astrophysical fluid flows, which can be run in one, two, and three dimensions, and supports a wide variety of physics, including hydrodynamics, ideal and non-ideal magnetohydrodynamic, N-body dynamics, primordial gas chemistry, optically thin radiative cooling of primordial and metal-enriched plasmas, and models for star formation and feedback in a cosmological context.