Particle theory

Abstract:

We study the supersymmetric particle spectra and LHC collider observables for the large-volume string models with a fundamental scale of 10^{11} GeV that arise in moduli-fixed string compactifications with branes and fluxes. The presence of magnetic fluxes on the brane world volume, required for chirality, perturb the soft terms away from those previously computed in the dilute-flux limit. We use the difference in high-scale gauge couplings to estimate the magnitude of this perturbation and study the potential effects of the magnetic fluxes by generating many random spectra with the soft terms perturbed around the dilute flux limit. Even with a 40% variation in the high-scale soft terms the low-energy spectra take a clear and predictive form. The resulting spectra are broadly similar to those arising on the SPS1a slope, but more degenerate. In their minimal version the models predict the ratios of gaugino masses to be M_1 : M_2 : M_3=(1.5 - 2) : 2 : 6, different to both mSUGRA and mirage mediation. Among the scalars, the squarks tend to be lighter and the sleptons heavier than for comparable mSUGRA models. We generate 10 fb^{-1} of sample LHC data for the random spectra in order to study the range of collider phenomenology that can occur. We perform a detailed mass reconstruction on one example large-volume string model spectrum. 100 fb^{-1} of integrated luminosity is sufficient to discriminate the model from mSUGRA and aspects of the sparticle spectrum can be accurately reconstructed.

Abstract:

We develop rigorous, analytic techniques to study the behaviour of biased random walks on combs. This enables us to calculate exactly the spectral dimension of random comb ensembles for any bias scenario in the teeth or spine. Two specific examples of random comb ensembles are discussed; the random comb with nonzero probability of an infinitely long tooth at each vertex on the spine and the random comb with a power law distribution of tooth lengths. We also analyze transport properties along the spine for these probability measures.