The analysis of complex multiphysics astrophysical simulations presents a unique and rapidly growing set of challenges: reproducibility, parallelization, and vast increases in data size and complexity chief among them. In order to meet these challenges, and in order to open up new avenues for collaboration between users of multiple simulation platforms, we present yt a , an open source, communitydeveloped astrophysical analysis and visualization toolkit. Analysis and visualization with yt are oriented around physically relevant quantities rather than quantities native to astrophysical simulation codes. While originally designed for handling Enzo's structure adaptive mesh refinement (AMR) data, yt has been extended to work with several different simulation methods and simulation codes including Orion, RAMSES, and FLASH. We report on 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. Furthermore, we discuss the underlying algorithms yt uses for processing and visualizing data, and its mechanisms for parallelization of analysis tasks.

This paper describes the open-source code Enzo, which uses block-structured adaptive mesh refinement to provide high spatial and temporal resolution for modeling astrophysical fluid flows. The code is Cartesian, can be run in 1, 2, and 3 dimensions, and supports a wide variety of physics including hydrodynamics, ideal and non-ideal magnetohydrodynamics, N-body dynamics (and, more broadly, self-gravity of fluids and particles), primordial gas chemistry, optically-thin radiative cooling of primordial and metal-enriched plasmas (as well as some optically-thick cooling models), radiation transport, cosmological expansion, and models for star formation and feedback in a cosmological context. In addition to explaining the algorithms implemented, we present solutions for a wide range of test problems, demonstrate the code's parallel performance, and discuss the Enzo collaboration's code development methodology.

Enzo (Enzo Developers, 2019a) is a block-structured adaptive mesh refinement code that is widely used to simulate astrophysical fluid flows (primarily, but not exclusively, cosmological structure formation, star formation, and turbulence). The code is a community project with dozens of users, and has contributed to hundreds of peer-reviewed publications in astrophysics, physics, and computer science. The code utilizes a Cartesian mesh can be run in one, two, or

The recent implementation of radiative transfer algorithms in numerous hydrodynamics codes has led to a dramatic improvement in studies of feedback in various astrophysical environments. However, because of methodological limitations and computational expense, the spectra of radiation sources are generally sampled at only a few evenly-spaced discrete emission frequencies. Using one-dimensional radiative transfer calculations, we investigate the discrepancies in gas properties surrounding model stars and accreting black holes that arise solely due to spectral discretization. We find that even in the idealized case of a static and uniform density field, commonly used discretization schemes induce errors in the neutral fraction and temperature by factors of two to three on average, and by over an order of magnitude in certain column density regimes. The consequences are most severe for radiative feedback operating on large scales, dense clumps of gas, and media consisting of multiple chemical species. We have developed a method for optimally constructing discrete spectra, and show that for two test cases of interest, carefully chosen four-bin spectra can eliminate errors associated with frequency resolution to high precision. Applying these findings to a fully three-dimensional radiation-hydrodynamic simulation of the early universe, we find that the H II region around a primordial star is substantially altered in both size and morphology, corroborating the one-dimensional prediction that discrete spectral energy distributions can lead to sizable inaccuracies in the physical properties of a medium, and as a result, the subsequent evolution and observable signatures of objects embedded within it.