Modeling the Anisotropic Two-Point Galaxy Correlation Function on Small Scales and Improved Measurements of H(z), D_A(z), and f(z)sigma_8(z) from the Sloan Digital Sky Survey DR7 Luminous Red Galaxies

We present a simple and efficient phenomenological model for the two-dimensional two-point galaxy correlation function that works well over a wide range of scales, from large scales down to scales as small as 25Mpc/h. Our model incorporates nonlinear effects, a scale-dependent galaxy bias on small scales, and allows the redshift-space distortions to be scale and direction dependent. We validate our model using LasDamas mock catalogs, and apply it to the Sloan Digital Sky Survey (SDSS) DR7 Luminous Red Galaxies (LRGs). Using only the monopole and quadrupole of the correlation function measured from the SDSS DR7 LRGs, we obtain improved measurements H(z)r_s(z_d)/c=0.0433\pm 0.0042, D_A(z)/r_s(z_d)=6.59\pm 0.46, and f(z)sigma_8(z)=0.429\pm 0.089 at z=0.35, using the scale range of 25<s<120Mpc/h. We expect our results and model to be useful in tightening dark energy and gravity constraints from the full analysis of current and future galaxy clustering data.Comment: 9 pages, 5 figures, accepted by MNRAS, the last version matches accepted one. arXiv admin note: substantial text overlap with arXiv:1205.5573, arXiv:1102.225

Using Multipoles of the Correlation Function to Measure H(z), D_A(z), and \beta(z) from Sloan Digital Sky Survey Luminous Red Galaxies

Galaxy clustering data can be used to measure the cosmic expansion history H(z), the angular-diameter distance D_A(z), and the linear redshift-space distortion parameter beta(z). Here we present a method for using effective multipoles of the galaxy two-point correlation function (\xi_0(s), \xi_2(s), \xi}_4(s), and \xi_6(s), with s denoting the comoving separation) to measure H(z), D_A(z)$, and beta(z), and validate it using LasDamas mock galaxy catalogs. Our definition of effective multipoles explicitly incorporates the discreteness of measurements, and treats the measured correlation function and its theoretical model on the same footing. We find that for the mock data, \xi_0+\xi_2+\xi_4 captures nearly all the information, and gives significantly stronger constraints on H(z), D_A(z), and beta(z), compared to using only \xi_0+\xi_2. We apply our method to the sample of luminous red galaxies (LRGs) from the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) without assuming a dark energy model or a flat Universe. We find that \xi}_4(s) deviates on scales of s<60Mpc/h from the measurement from mock data (in contrast to \xi_0(s), \xi_2(s), and \xi_6(s)), thus we only use \xi_0+\xi_2 for our fiducial constraints. We obtain {H(0.35), D_A(0.35), Omega_mh^2, beta(z)} = {79.6_{-8.7}^{+8.3} km/s/Mpc, 1057_{-87}^{+88}Mpc, 0.103\pm0.015, 0.44\pm0.15} using \xi_0+\xi_2. We find that H(0.35)r_s(z_d)/c and D_A(0.35)/r_s(z_d) (where r_s(z_d) is the sound horizon at the drag epoch) are more tightly constrained: {H(0.35)r_s(z_d)/c, D_A(0.35)/r_s(z_d)} = {0.0437_{-0.0043}^{+0.0041}, 6.48_{-0.43}^{+0.44}\} using \xi_0+\xi_2.Comment: 12 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1102.225

A digital definition method for manufacturing model of aircraft integral panel

The manufacturing model of aircraft integral panel is presented based on the analysis of its structure and manufacturing process. The manufacturing model for each key process consists of model for processing and model for workpiece to bridge digital design and fabricating. Model for workpiece is used to express the target part information at the end of some operation. Model for processing is used to describe the intermediate state information, and it aims to attain but is different the workpiece because of process factors. The definition flow of the manufacturing model is given. The modeling approach of integral panel part blank from shot peen forming part model orienting to NC cutting is proposed and exemplified. It is analyzed that the approaches above can define the models accurately and totally to meet the needs of process planning , NC fabricating and inspecting

The influencing mechanism of manufacturing scene change on process domain knowledge reuse

It is necessary for a enterprise to reuse outside process domain knowledge to develop intelligent manufacturing technology. The key factors influencing knowledge reuse in digital manufacturing scene are manufacturing activities and PPR (Products, Processes and Resources) related to knowledge modeling, enterprise and integrated systems related to knowledge utilizing. How these factors influence knowledge modeling and utilizing is analyzed. Process domain knowledge reuse across the enterprises consists of knowledge reconfiguration and integrated application with CAx systems. The module-based knowledge model and loosely-coupled integration application of process domain knowledge are proposed. The aircraft sheet metal process domain knowledge reuse is taken as an example, and it shows that the knowledge reuse process can be made flexible and rapid