Note on Inversion Formula to Determine Binary Elements by Astrometry

Simplified solutions to determine binary elements by astrometry were obtained in terms of elementary functions (Asada et al. 2004), and therefore require neither iterative nor numerical methods. In the framework of the simplified solution, this paper discusses the remaining two parameters of the time of periastron passage and the longitude of ascending node in order to complete the solution. We thus clarify a difference between the simplified solution and other analytical methods.Comment: 12 pages, 4 figures, 2 tables; accepted for publication in PAS

Interpretations of SUSY Searches in ATLAS with Simplified Models

We present the status of interpretations of Supersymmetry (SUSY) searches in ATLAS at the Large Hadron Collider (LHC) using simplified models. Such models allow a systematic scan through the phase space in the sparticle mass plane, and in the corresponding final state kinematics. Models at various levels of simplification have been studied in ATLAS. The results can be extrapolated to more general new physics models which lead to the same event topology with similar mass hierarchies. Searches in the no-lepton channel with 1.04 fb^-1 of data from 2011 and the same-sign dilepton channel with 35 pb^-1 of data from 2010 are presented. No excess above the Standard Model expectation is observed, and the results are interpreted using the simplified models.Comment: 9 pages, 7 figures, 3 tables, for the proceedings of the DPF-2011 conference, Providence, RI, August 9-13, 201

Vectorization and Parallelization of the Adaptive Mesh Refinement N-body Code

In this paper, we describe our vectorized and parallelized adaptive mesh refinement (AMR) N-body code with shared time steps, and report its performance on a Fujitsu VPP5000 vector-parallel supercomputer. Our AMR N-body code puts hierarchical meshes recursively where higher resolution is required and the time step of all particles are the same. The parts which are the most difficult to vectorize are loops that access the mesh data and particle data. We vectorized such parts by changing the loop structure, so that the innermost loop steps through the cells instead of the particles in each cell, in other words, by changing the loop order from the depth-first order to the breadth-first order. Mass assignment is also vectorizable using this loop order exchange and splitting the loop into $2^{N_{dim}}$ loops, if the cloud-in-cell scheme is adopted. Here, $N_{dim}$ is the number of dimension. These vectorization schemes which eliminate the unvectorized loops are applicable to parallelization of loops for shared-memory multiprocessors. We also parallelized our code for distributed memory machines. The important part of parallelization is data decomposition. We sorted the hierarchical mesh data by the Morton order, or the recursive N-shaped order, level by level and split and allocated the mesh data to the processors. Particles are allocated to the processor to which the finest refined cells including the particles are also assigned. Our timing analysis using the $\Lambda$-dominated cold dark matter simulations shows that our parallel code speeds up almost ideally up to 32 processors, the largest number of processors in our test.Comment: 21pages, 16 figures, to be published in PASJ (Vol. 57, No. 5, Oct. 2005