Ram pressure stripping can remove hot and cold gas from galaxies in the intracluster medium (ICM), as shown by observations of X-ray and HI galaxy wakes in nearby clusters of galaxies. However, ram pressure stripping, including pre-processing in grou
p environments, does not remove all the hot coronal gas from cluster galaxies. Recent high-resolution Chandra observations have shown that $sim 1 - 4$ kpc extended, hot galactic coronae are ubiquitous in group and cluster galaxies. To better understand this result, we simulate ram pressure stripping of a cosmologically motivated population of galaxies in isolated group and cluster environments. The galaxies and the host group and cluster are composed of collisionless dark matter and hot gas initially in hydrostatic equilibrium with the galaxy and host potentials. We show that the rate at which gas is lost depends on the galactic and host halo mass. Using synthetic X-ray observations, we evaluate the detectability of stripped galactic coronae in real observations by stacking images on the known galaxy centers. We find that coronal emission should be detected within $sim 10$ arcsec, or $sim 5$ kpc up to $sim 2.3$ Gyr in the lowest (0.1 - 1.2 keV) energy band. Thus the presence of observed coronae in cluster galaxies significantly smaller than the hot X-ray halos of field galaxies indicates that at least some gas removal occurs within cluster environments for recently accreted galaxies. Finally, we evaluate the possibility that existing and future X-ray cluster catalogs can be used in combination with optical galaxy positions to detect galactic coronal emission via stacking analysis. We briefly discuss the effects of additional physical processes on coronal survival, and will address them in detail in future papers in this series.
Multiple datasets containing different types of features may be available for a given task. For instance, users profiles can be used to group users for recommendation systems. In addition, a model can also use users historical behaviors and credit hi
story to group users. Each dataset contains different information and suffices for learning. A number of clustering algorithms on multiple datasets were proposed during the past few years. These algorithms assume that at least one dataset is complete. So far as we know, all the previous methods will not be applicable if there is no complete dataset available. However, in reality, there are many situations where no dataset is complete. As in building a recommendation system, some new users may not have a profile or historical behaviors, while some may not have a credit history. Hence, no available dataset is complete. In order to solve this problem, we propose an approach called Collective Kernel Learning to infer hidden sample similarity from multiple incomplete datasets. The idea is to collectively completes the kernel matrices of incomplete datasets by optimizing the alignment of the shared instances of the datasets. Furthermore, a clustering algorithm is proposed based on the kernel matrix. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The proposed clustering algorithm outperforms the comparison algorithms by as much as two times in normalized mutual information.
Galaxies in clusters are more likely to be of early type and to have lower star formation rates than galaxies in the field. Recent observations and simulations suggest that cluster galaxies may be `pre-processed by group or filament environments and
that galaxies that fall into a cluster as part of a larger group can stay coherent within the cluster for up to one orbital period (`post-processing). We investigate these ideas by means of a cosmological $N$-body simulation and idealized $N$-body plus hydrodynamics simulations of a group-cluster merger. We find that group environments can contribute significantly to galaxy pre-processing by means of enhanced galaxy-galaxy merger rates, removal of galaxies hot halo gas by ram pressure stripping, and tidal truncation of their galaxies. Tidal distortion of the group during infall does not contribute to pre-processing. Post-processing is also shown to be effective: galaxy-galaxy collisions are enhanced during a groups pericentric passage within a cluster, the merger shock enhances the ram pressure on group and cluster galaxies, and an increase in local density during the merger leads to greater galactic tidal truncation.
Efficient computation of lattice defect geometries such as point defects, dislocations, disconnections, grain boundaries, interfaces and free surfaces requires accurate coupling of displacements near the defect to the long-range elastic strain. Flexi
ble boundary condition methods embedded a defect in infinite harmonic bulk through the lattice Green function. We demonstrate an efficient and accurate calculation of the lattice Green function from the force-constant matrix for general crystals with an arbitrary basis by extending a method for Bravais lattices. New terms appear due to the presence of optical modes and the possible loss of inversion symmetry. By separately treating poles and discontinuities in reciprocal space, numerical accuracy is controlled at all distances. We compute the lattice Green function for a two-dimensional model with broken symmetry to elucidate the role of different coupling terms. The algorithm is generally applicable in two and three dimensions, to crystals with arbitrary number of atoms in the unit cell, symmetry, and interactions.
We determine the stability and properties of interfaces of low-index Au surfaces adhered to TiO2(110), using density functional theory energy density calculations. We consider Au(100) and Au(111) epitaxies on rutile TiO2(110) surface, as observed in
experiments. For each epitaxy, we consider several different interfaces: Au(111)//TiO2(110) and Au(100)//TiO2(110), with and without bridging oxygen, Au(111) on 1x2 added-row TiO2(110) reconstruction, and Au(111) on a proposed 1x2 TiO reconstruction. The density functional theory energy density method computes the energy changes on each of the atoms while forming the interface, and evaluates the work of adhesion to determine the equilibrium interfacial structure.
We present results of high-resolution imaging toward HL Tau by the Combined Array for Research in Millimeter-wave Astronomy (CARMA). We have obtained 1.3 and 2.7 mm dust continua with an angular resolution down to 0.13 arc second. Through model fitti
ng to the two wavelength data simultaneously in Bayesian inference using a flared viscous accretion disk model, we estimate the physical properties of HL Tau, such as density distribution, dust opacity spectral index, disk mass, disk size, inclination angle, position angle, and disk thickness. HL Tau has a circumstellar disk mass of 0.13 solar mass, a characteristic radius of 79 AU, an inclination of 40 degree, and a position angle of 136 degree. Although a thin disk model is preferred by our two wavelength data, a thick disk model is needed to explain the high mid- and far-infrared emission of the HL Tau spectral energy distribution. This could imply large dust grains settled down on the mid plane with fine dust grains mixed with gas. The HL Tau disk is likely gravitationally unstable and can be fragmented between 50 and 100 AU of radius. However, we did not detect dust thermal continuum supporting the protoplanet candidate claimed by a previous study using observations of the Very Large Array at 1.3 cm.
We present results of the implementation of one MILC lattice QCD application-simulation with dynamical clover fermions using the hybrid-molecular dynamics R algorithm-on the Cell Broadband Engine processor. Fifty-four individual computational kernels
responsible for 98.8% of the overall execution time were ported to the Cells Synergistic Processing Elements (SPEs). The remaining application framework, including MPI-based distributed code execution, was left to the Cells PowerPC processor. We observe that we only infrequently achieve more than 10 GFLOPS with any of the kernels, which is just over 4% of the Cells peak performance. At the same time, many of the kernels are sustaining a bandwidth close to 20 GB/s, which is 78% of the Cells peak. This indicates that the application performance is limited by the bandwidth between the main memory and the SPEs. In spite of this limitation, speedups of 8.7x (for 8x8x16x16 lattice) and 9.6x (for 16x16x16x16 lattice) were achieved when comparing a 3.2 GHz Cell processor to a single core of a 2.33 GHz Intel Xeon processor. When comparing the code scaled up to execute on a dual-Cell blade and a quad-core dual-chip Intel Xeon blade, the speedups are 1.5x (8x8x16x16 lattice) and 4.1x (16x16x16x16 lattice).
