CP violating dimuon charge asymmetry in general left-right models

The recently measured charge asymmetry of like-sign dimuon events by the D0 collaboration at Tevatron shows the 3.9 \sigma\ deviation from the standard model prediction. In order to solve this mismatch, we investigate the right-handed current contributions to $B_s-\bar{B}_s$ and $B_d-\bar{B}_d$ mixings which are the major source of the like-sign dimuon events in $b \bar{b}$ production in general left-right models without imposing manifest or pseudo-manifest left-right symmetry. We find the allowed region of new physics parameters satisfying the current experimental data.Comment: 9 pages, 4 figure

Baryon magnetic moments in the external field method

We present a calculation of the magnetic moments of the baryon octet and decuplet using the external field method and standard Wilson gauge and fermion actions in the quenched approximation. Progressively smaller static magnetic fields are introduced on a $24^4$ latticeat beta=6.0 and the pion mass is probed down to about 500 MeV. Magnetic moments are extracted from the linear response of the masses to the external field.Comment: Lattice2004 (weak matrix elements), 3 pages, 8 figure

Finite-volume Hamiltonian method for coupled channel interactions in lattice QCD

Within a multi-channel formulation of $\pi\pi$ scattering, we investigate the use of the finite-volume Hamiltonian approach to resolve scattering observables from lattice QCD spectra. The asymptotic matching of the well-known L\"uscher formalism encodes a unique finite-volume spectrum. Nevertheless, in many practical situations, such as coupled-channel systems, it is advantageous to interpolate isolated lattice spectra in order to extract physical scattering parameters. Here we study the use of the Hamiltonian framework as a parameterisation that can be fit directly to lattice spectra. We find that with a modest amount of lattice data, the scattering parameters can be reproduced rather well, with only a minor degree of model dependence.Comment: 25 pages, 16 figure

Microwave characterization of slotline on high resistivity silicon for antenna feed network

Conventional silicon wafers have low resistivity and consequently unacceptably high value of dielectric attenuation constant. Microwave circuits for phased array antenna systems fabricated on these wafers therefore have low efficiency. By choosing a silicon substrate with sufficiently high resistivity it is possible to make the dielectric attenuation constant of the interconnecting microwave transmission lines approach those of GaAs or InP. In order for this to be possible, the transmission lines must be characterized. In this presentation, the effective dielectric constant (epsilon sub eff) and attenuation constant (alpha) of a slotline on high resistivity (5000 to 10 000 ohm-cm) silicon wafer will be discussed. The epsilon sub eff and alpha are determined from the measured resonant frequencies and the corresponding insertion loss of a slotline ring resonator. The results for slotline will be compared with microstrip line and coplanar waveguide

Neighborhood Social Capital and Social Learning for Experience Attributes of Products

Social learning can occur when information is transferred from existing customers to potential customers. It is especially important when the information that is conveyed pertains to experience attributes, i.e., attributes of products that cannot be fully verified prior to the first purchase. Experience attributes are prevalent and salient when consumers shop through catalogs, on home shopping networks, and over the Internet. Firms therefore employ creative and sometimes costly methods to help consumers resolve uncertainty; we argue that uncertainty can be partially resolved through social learning processes that occur naturally and emanate from local neighborhood characteristics. Using data from Bonobos, a leading U.S. online fashion retailer, we find not only that local social learning facilitates customer trial but also that the effect is economically important because about half of all trials were partially attributable to it. Merging data from the Social Capital Community Benchmark Survey, we find that neighborhood social capital, i.e., the propensity for neighbors to trust each other and communicate with each other, enhances the social learning process and makes it more efficient. Social capital does not operate on trials directly; rather, it improves the learning process and therefore indirectly drives sales when what is communicated is favorable