Optimized Multi-Frequency Spectra for Applications in Radiative Feedback and Cosmological Reionization

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 HII 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.Comment: 15 pages, 13 figures, 2 tables, accepted for publication in the Astrophysical Journa

Parallel HOP: A Scalable Halo Finder for Massive Cosmological Data Sets

Modern N-body cosmological simulations contain billions ($10^9$) of dark matter particles. These simulations require hundreds to thousands of gigabytes of memory, and employ hundreds to tens of thousands of processing cores on many compute nodes. In order to study the distribution of dark matter in a cosmological simulation, the dark matter halos must be identified using a halo finder, which establishes the halo membership of every particle in the simulation. The resources required for halo finding are similar to the requirements for the simulation itself. In particular, simulations have become too extensive to use commonly-employed halo finders, such that the computational requirements to identify halos must now be spread across multiple nodes and cores. Here we present a scalable-parallel halo finding method called Parallel HOP for large-scale cosmological simulation data. Based on the halo finder HOP, it utilizes MPI and domain decomposition to distribute the halo finding workload across multiple compute nodes, enabling analysis of much larger datasets than is possible with the strictly serial or previous parallel implementations of HOP. We provide a reference implementation of this method as a part of the toolkit yt, an analysis toolkit for Adaptive Mesh Refinement (AMR) data that includes complementary analysis modules. Additionally, we discuss a suite of benchmarks that demonstrate that this method scales well up to several hundred tasks and datasets in excess of $2000^3$ particles. The Parallel HOP method and our implementation can be readily applied to any kind of N-body simulation data and is therefore widely applicable.Comment: 29 pages, 11 figures, 2 table

A Multi-Code Analysis Toolkit for Astrophysical Simulation Data

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 (available at http://yt.enzotools.org/), an open source, community-developed 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.Comment: 18 pages, 6 figures, emulateapj format. Resubmitted to Astrophysical Journal Supplement Series with revisions from referee. yt can be found at http://yt.enzotools.org

Investigating a method of producing "Red and Dead" galaxies

In optical wavelengths, galaxies are observed to be either red or blue. The overall color of a galaxy is due to the distribution of the ages of its stellar population. Galaxies with currently active star formation appear blue, while those with no recent star formation at all (greater than about a Gyr) have only old, red stars. This strong bimodality has lead to the idea of star formation quenching, and various proposed physical mechanisms. In this dissertation, I attempt to reproduce with Enzo the results of Naab et al. (2007), in which red and dead galaxies are formed using gravitational quenching,rather than with one of the more typical methods of quenching. My initial attempts are unsuccessful, and I explore the reasons why I think they failed. Then using simpler methods better suited to Enzo + AMR, I am successful in producing a galaxy that appears to be similar in color and formation history to those in Naab et al. However, quenching is achieved using unphysically high star formation efficiencies, which is a different mechanism than Naab et al. suggests. Preliminary results of a much higher resolution, follow-on simulation of the above show some possible contradiction with the results of Naab et al. Cold gas is streaming into the galaxy to fuel starbursts, while at a similar epoch the galaxies in Naab et al. have largely already ceased forming stars in the galaxy. On the other hand, the results of the high resolution simulation are qualitatively similar to other works in the literature that show a somewhat different gravitational quenching mechanism than Naab et al. I also discuss my work using halo finders to analyze simulated cosmological data, and my work improving the Enzo/AMR analysis tool "yt". This includes two parallelizations of the halo finder HOP which allows analysis of very large cosmological datasets on parallel machines. The first version is "yt-HOP," which works well for datasets between about 256³ and 512³ particles, but has memory bottlenecks as the datasets get larger. These bottlenecks inspired the second version, "Parallel HOP," which is a fully parallelized method and implementation of HOP that has worked on datasets with more than 2048³ particles on hundreds of processing cores. Both methods are described in detail, as are the various effects of performance- related runtime options. Additionally, both halo finders are subjected to a full suite of performance benchmarks varying both dataset sizes and computational resources used. I conclude with descriptions of four new tools I added to yt. A Parallel Structure Function Generator allows analysis of two-point functions, such as correlation functions, using memory- and workload- parallelism. A Parallel Merger Tree Generator leverages the parallel halo finders in yt, such as Parallel HOP, to build the merger tree of halos in a cosmological simulation, and outputs the result to a SQLite database for simple and powerful data extraction. A Star Particle Analysis toolkit takes a group of star particles and can output the rate of formation as a function of time, and/or a synthetic Spectral Energy Distribution (S.E.D.) using the Bruzual and Charlot (2003) data tables. Finally, a Halo Mass Function toolkit takes as input a list of halo masses and can output the halo mass function for the halos, as well as an analytical fit for those halos using several previously published fit