Kaylea Nelson

HYDRODYNAMIC SIMULATION OF NON-THERMAL PRESSURE PROFILES OF GALAXY CLUSTERS

Cosmological constraints from X-ray and microwave observations of galaxy clusters are subjected to systematic uncertainties. Non-thermal pressure support due to internal gas motions in galaxy clusters is one of the major sources of astrophysical uncertainties. Using a mass-limited sample of galaxy clusters from a high-resolution hydrodynamical cosmological simulation, we characterize the non-thermal pressure fraction profile and study its dependence on redshift, mass, and mass accretion rate. We find that the non-thermal pressure fraction profile is universal across redshift when galaxy cluster radii are defined with respect to the mean matter density of the universe instead of the commonly used critical density. We also find that the non-thermal pressure is predominantly radial, and the gas velocity anisotropy profile exhibits strong universality when galaxy cluster radii are defined with respect to the mean matter density of the universe. However, we find that the non-thermal pressure fraction is strongly dependent on the mass accretion rate of the galaxy cluster. We provide fitting formulae for the universal non-thermal pressure fraction and velocity anisotropy profiles of gas in galaxy clusters, which should be useful in modeling astrophysical uncertainties pertinent to using galaxy clusters as cosmological probes.

EVOLUTION OF THE MERGER-INDUCED HYDROSTATIC MASS BIAS IN GALAXY CLUSTERS

In this work, we examine the effects of mergers on the hydrostatic mass estimate of galaxy clusters using high-resolution Eulerian cosmological simulations. We utilize merger trees to isolate the last merger for each cluster in our sample and follow the time evolution of the hydrostatic mass bias as the systems relax. We find that during a merger, a shock propagates outward from the parent cluster, resulting in an overestimate in the hydrostatic mass bias. After the merger, as a cluster relaxes, the bias in hydrostatic mass estimate decreases but remains at a level of ?5%-10% with 15%-20% scatter within r 500. We also investigate the post-merger evolution of the pressure support from bulk motions, a dominant cause of this residual mass bias. At r 500, the contribution from random motions peaks at 30% of the total pressure during the merger and quickly decays to ~10%-15% as a cluster relaxes. Additionally, we use a measure of the random motion pressure to correct the hydrostatic mass estimate. We discover that 4?Gyr after mergers, the direct effects of the merger event on the hydrostatic mass bias have become negligible. Thereafter, the mass bias is primarily due to residual bulk motions in the gas which are not accounted for in the hydrostatic equilibrium equation. We present a hydrostatic mass bias correction method that can recover the unbiased cluster mass for relaxed clusters with 9% scatter at r 500 and 11% scatter in the outskirts, within r 200.

nIFTy galaxy cluster simulations – I. Dark matter and non-radiative models

We have simulated the formation of a galaxy cluster in a Ʌ cold dark matter universe using 13 different codes modelling only gravity and non-radiative hydrodynamics (RAMSES, ART, AREPO, HYDRA and nine incarnations of GADGET). This range of codes includes particle-based, moving and fixed mesh codes as well as both Eulerian and Lagrangian fluid schemes. The various GADGET implementations span classic and modern smoothed particle hydrodynamics (SPH) schemes. The goal of this comparison is to assess the reliability of cosmological hydrodynamical simulations of clusters in the simplest astrophysically relevant case, that in which the gas is assumed to be non-radiative. We compare images of the cluster at z = 0, global properties such as mass and radial profiles of various dynamical and thermodynamical quantities. The underlying gravitational framework can be aligned very accurately for all the codes allowing a detailed investigation of the differences that develop due to the various gas physics implementations employed. As expected, the mesh-based codes RAMSES, ART and AREPO form extended entropy cores in the gas with rising central gas temperatures. Those codes employing classic SPH schemes show falling entropy profiles all the way into the very centre with correspondingly rising density profiles and central temperature inversions. We show that methods with modern SPH schemes that allow entropy mixing span the range between these two extremes and the latest SPH variants produce gas entropy profiles that are essentially indistinguishable from those obtained with grid-based methods.

Temperature structure of the intracluster medium from smoothed-particle hydrodynamics and adaptive-mesh refinement simulations

Analyses of cosmological hydrodynamic simulations of galaxy clusters suggest that X-ray masses can be underestimated by 10% to 30%. The largest bias originates by both violation of hydrostatic equilibrium and an additional temperature bias caused by inhomogeneities in the X-ray emitting intra-cluster medium (ICM). To elucidate on this large dispersion among theoretical predictions, we evaluate the degree of temperature structures in cluster sets simulated either with smoothed-particle-hydrodynamics (SPH) and adaptive-mesh-refinement (AMR) codes. We find that the SPH simulations produce larger temperature variations connected to the persistence of both substructures and their stripped cold gas. This difference is more evident in no-radiative simulations, while it is reduced in the presence of radiative cooling. We also find that the temperature variation in radiative cluster simulations is generally in agreement with the observed one in the central regions of clusters. Around R500 the temperature inhomogeneities of the SPH simulations can generate twice the typical hydrostatic-equilibrium mass bias of the AMR sample. We emphasize that a detailed understanding of the physical processes responsible for the complex thermal structure in ICM requires improved resolution and high sensitivity observations in order to extend the analysis to higher temperature systems and larger cluster-centric radii. Subject headings: galaxies: clusters: general ‐ galaxies: clusters: intracluster medium ‐ X-rays: galaxies: clusters ‐ methods: numerical

PREDICTING MERGER-INDUCED GAS MOTIONS IN ΛCDM GALAXY CLUSTERS

In the hierarchical structure formation model, clusters of galaxies form through a sequence of mergers and continuous mass accretion, which generate significant random gas motions especially in their outskirts where material is actively accreting. Non-thermal pressure provided by the internal gas motions affects the thermodynamic structure of the X-ray emitting intracluster plasma and introduces biases in the physical interpretation of X-ray and Sunyaev-Zeldovich effect observations. However, we know very little about the nature of gas motions in galaxy clusters. The ASTRO-H X-ray mission, scheduled to launch in 2015, will have a calorimeter capable of measuring gas motions in galaxy clusters at the level of 100 km s–1. In this work, we predict the level of merger-induced gas motions expected in the ΛCDM model using hydrodynamical simulations of galaxy cluster formation. We show that the gas velocity dispersion is larger in more massive clusters, but exhibits a large scatter. We show that systems with large gas motions are morphologically disturbed, while early forming, relaxed groups show a smaller level of gas motions. By analyzing mock ASTRO-H observations of simulated clusters, we show that such observations can accurately measure the gas velocity dispersion out to the outskirts of nearby relaxed galaxy clusters. ASTRO-H analysis of merging clusters, on the other hand, requires multi-component spectral fitting and enables unique studies of substructures in galaxy clusters by measuring both the peculiar velocities and the velocity dispersion of gas within individual sub-clusters.

THE RELATION BETWEEN GAS DENSITY AND VELOCITY POWER SPECTRA IN GALAXY CLUSTERS: QUALITATIVE TREATMENT AND COSMOLOGICAL SIMULATIONS

We address the problem of evaluating the power spectrum of the velocity field of the intracluster medium using only information on the plasma density fluctuations, which can be measured today by Chandra and XMM-Newton observatories. We argue that for relaxed clusters there is a linear relation between the rms density and velocity fluctuations across a range of scales, from the largest ones, where motions are dominated by buoyancy, down to small, turbulent scales: , where δρ k /ρ is the spectral amplitude of the density perturbations at wavenumber k, is the mean square component of the velocity field, cs is the sound speed, and η1 is a dimensionless constant of the order of unity. Using cosmological simulations of relaxed galaxy clusters, we calibrate this relation and find η1 1 ± 0.3. We argue that this value is set at large scales by buoyancy physics, while at small scales the density and velocity power spectra are proportional because the former are a passive scalar advected by the latter. This opens an interesting possibility to use gas density power spectra as a proxy for the velocity power spectra in relaxed clusters across a wide range of scales.

Sunyaev–Zeldovich signal processing and temperature–velocity moment method for individual clusters

Future high resolution, high sensitivity Sunyaev-Zeldovich (SZ) observations of individual clusters will provide an exciting opportunity to answer specific questions about the dynamical state of the intra-cluster medium (ICM). In this paper we develop a new method that clearly shows the connection of the SZ signal with the underlying cluster model. We include relativistic temperature and kinematic corrections in the single-scattering approximation, allowing studies of hot clusters. In our approach, particular moments of the temperature and velocity field along the line-of-sight determine the precise spectral shape and morphology of the SZ signal. We illustrate how to apply our method to different cluster models, highlighting parameter degeneracies and instrumental effects that are important for interpreting future high-resolution SZ data. Our analysis shows that line-of-sight temperature variations can introduce significant biases in the derived SZ temperature and peculiar velocity. We furthermore discuss how the position of the SZ null is affected by the clusters temperature and velocity structure. Our computations indicate that the SZ signal around the null alone is rather insensitive to different cluster models and that high frequency channels add a large leverage in this respect. We also apply our method to recent high sensitivity SZ data of the Bullet cluster, showing how the results can be linked to line-of-sight variations in the electron temperature. The tools developed here as part of SZpack should be useful for analyzing high-resolution SZ data and computing SZ maps from simulated clusters.

AutoMorph: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction

Large-scale, comparative studies of morphological variation are rare due to the time-intensive nature of shape quantification. This data gap is important to address, as intraspecific and interspecific morphological variation underpins and reflects ecological and evolutionary processes. Here, we detail a novel software package, AutoMorph, for high-throughput object and shape extraction. AutoMorph can batch image many types of organisms (e.g. foraminifera, molluscs and fish teeth), allowing for rapid generation of assemblage-scale morphological data. We used AutoMorph to image and generate 2D and 3D morphological data for >100,000 marine microfossils in about a year. Our collaborators have used AutoMorph to process >12,000 patellogastropod shells and >50,000 fish teeth. AutoMorph allows users to rapidly produce large amounts of morphological data, facilitating community-scale evolutionary and ecological studies. To hasten the adoption of automated approaches, we have made AutoMorph freely available and open source. AutoMorph runs on all UNIX-like systems; future versions will run across all platforms.

Sixty-one thousand recent planktonic foraminifera from the Atlantic Ocean

Marine microfossils record the environmental, ecological, and evolutionary dynamics of past oceans in temporally expanded sedimentary archives. Rapid imaging approaches provide a means of exploiting the primary advantage of this archive, the vast number of fossils, for evolution and ecology. Here we provide the first large scale image and 2D and 3D shape dataset of modern planktonic foraminifera, a major microfossil group, from 34 Atlantic Ocean sediment samples. Information on more than 124,000 objects is provided, including general object classification for 4/5ths of the dataset (~ 99,000 objects). Of the ~ 99,000 classifications provided, more than 61,000 are complete or damaged planktonic foraminifera. Objects also include benthic foraminifera, ostracods, pteropods, spicules, and planktonic foraminifera test fragments, among others. This dataset is the first major microfossil output of a new high-throughput imaging method (AutoMorph) developed to extract 2D and 3D data from photographic images of fossils. Our sample preparation and imaging techniques are described in detail. The data provided here comprises the most extensive publically available archive of planktonic foraminiferal morphology and morphological variation to date.