Electronic Structures of Antiperovskite Superconductor MgCNi3_3 and Related Compounds

Electronic structure of a newly discovered antiperovskite superconductor MgCNi$_3$ is investigated by using the LMTO band method. The main contribution to the density of states (DOS) at the Fermi energy $E_{\rm F}$ comes from Ni 3$d$ states which are hybridized with C 2$p$ states. The DOS at $E_{\rm F}$ is varied substantially by the hole or electron doping due to the very high and narrow DOS peak located just below $E_{\rm F}$. We have also explored electronic structures of C-site and Mg-site doped MgCNi$_3$ systems, and described the superconductivity in terms of the conventional phonon mechanism.Comment: 3 pages, presented at ORBITAL2001 September 11-14, 2001 (Sendai, JAPAN

Electronic structures of antiperovskite superconductors: MgXNi3_3 (X=B,C,N)

We have investigated electronic structures of a newly discovered antiperovskite superconductor MgCNi$_3$ and related compounds MgBNi$_3$ and MgNNi$_3$. In MgCNi$_3$, a peak of very narrow and high density of states is located just below $\rm E_F$, which corresponds to the $\pi^*$ antibonding state of Ni-3d and C-$2p$ but with the predominant Ni-3d character. The prominent nesting feature is observed in the $\Gamma$-centered electron Fermi surface of an octahedron-cage-like shape that originates from the 19th band. The estimated superconducting parameters based on the simple rigid-ion approximation are in reasonable agreement with experiment, suggesting that the superconductivity in MgCNi$_3$ is described well by the conventional phonon mechanism.Comment: 5 pages, 5 figure

Electronic structure of metallic antiperovskite compound GaCMn3_3

We have investigated electronic structures of antiperovskite GaCMn$_3$ and related Mn compounds SnCMn$_3$, ZnCMn$_3$, and ZnNMn$_3$. In the paramagnetic state of GaCMn$_3$, the Fermi surface nesting feature along the $\Gamma{\rm R}$ direction is observed, which induces the antiferromagnetic (AFM) spin ordering with the nesting vector {\bf Q} $\sim \Gamma{\rm R}$. Calculated susceptibilities confirm the nesting scenario for GaCMn$_3$ and also explain various magnetic structures of other antiperovskite compounds. Through the band folding effect, the AFM phase of GaCMn$_3$ is stabilized. Nearly equal densities of states at the Fermi level in the ferromagnetic and AFM phases of GaCMn$_3$ indicate that two phases are competing in the ground state.Comment: 4 pages, 5 figure

Anomalous double peak structure in Nb/Ni superconductor/ferromagnet tunneling DOS

We have experimentally investigated the density of states (DOS) in Nb/Ni (S/F) bilayers as a function of Ni thickness, $d_F$. Our thinnest samples show the usual DOS peak at $\pm\Delta_0$, whereas intermediate-thickness samples have an anomalous ``double-peak'' structure. For thicker samples ($d_F \geq 3.5$ nm), we see an ``inverted'' DOS which has previously only been reported in superconductor/weak-ferromagnet structures. We analyze the data using the self-consistent non-linear Usadel equation and find that we are able to quantitatively fit the features at $\pm\Delta_0$ if we include a large amount of spin-orbit scattering in the model. Interestingly, we are unable to reproduce the sub-gap structure through the addition of any parameter(s). Therefore, the observed anomalous sub-gap structure represents new physics beyond that contained in the present Usadel theory.Comment: 4 pages, 3 figure

Astronomy in the Cloud: Using MapReduce for Image Coaddition

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computation challenges such as anomaly detection and classification, and moving object tracking. Since such studies benefit from the highest quality data, methods such as image coaddition (stacking) will be a critical preprocessing step prior to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources or transient objects, these data streams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this paper we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data is partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources, e.g., Amazon's EC2. We report on our experience implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multi-terabyte imaging dataset provides a good testbed for algorithm development since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image coaddition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table