M
Matthew J. Turk
Researcher at University of Illinois at Urbana–Champaign
Publications - 101
Citations - 9103
Matthew J. Turk is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Star formation & Galaxy. The author has an hindex of 32, co-authored 101 publications receiving 8089 citations. Previous affiliations of Matthew J. Turk include Columbia University & National Center for Supercomputing Applications.
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Journal ArticleDOI
yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data
Matthew J. Turk,Britton D. Smith,Jeffrey S. Oishi,Stephen Skory,Samuel W. Skillman,Tom Abel,Michael L. Norman +6 more
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.
Journal ArticleDOI
A Multi-Code Analysis Toolkit for Astrophysical Simulation Data
Matthew J. Turk,Britton D. Smith,Jeffrey S. Oishi,Stephen Skory,Samuel W. Skillman,Tom Abel,Michael L. Norman +6 more
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).
Journal ArticleDOI
Enzo: an adaptive mesh refinement code for astrophysics
Greg L. Bryan,Michael L. Norman,Brian W. O'Shea,Tom Abel,John H. Wise,Matthew J. Turk,Daniel R. Reynolds,David C. Collins,Peng Wang,Peng Wang,Samuel W. Skillman,Samuel W. Skillman,Britton D. Smith,Britton D. Smith,Robert P. Harkness,James Bordner,Ji-hoon Kim,Michael Kuhlen,Hao Xu,Nathan J. Goldbaum,Cameron Hummels,Alexei G. Kritsuk,Elizabeth J. Tasker,Stephen Skory,Christine M. Simpson,Oliver Hahn,Jeffrey S. Oishi,Geoffrey C. So,Fen Zhao,Renyue Cen,Yuan Li +30 more
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
Greg L. Bryan,Michael L. Norman,Brian W. O'Shea,Tom Abel,John H. Wise,Matthew J. Turk,Daniel R. Reynolds,David C. Collins,Peng Wang,Samuel W. Skillman,Britton D. Smith,Robert P. Harkness,James Bordner,Ji-hoon Kim,Michael Kuhlen,Hao Xu,Nathan J. Goldbaum,Cameron Hummels,Alexei G. Kritsuk,Elizabeth J. Tasker,Stephen Skory,Christine M. Simpson,Oliver Hahn,Jeffrey S. Oishi,Geoffrey C. So,Fen Zhao,Renyue Cen,Yuan Li +27 more
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
Greg L. Bryan,Michael L. Norman,Brian W. O'Shea,Tom Abel,John H. Wise,Matthew J. Turk,Daniel R. Reynolds,David C. Collins,Peng Wang,Samuel W. Skillman,Britton D. Smith,Robert P. Harkness,James Bordner,Ji-hoon Kim,Michael Kuhlen,Hao Xu,Nathan J. Goldbaum,Cameron Hummels,Alexei G. Kritsuk,Elizabeth J. Tasker,Stephen Skory +20 more
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.