We present a study of the pseudoscalar and vector meson form factors, calculated using the Fat-Link Irrelevant Clover (FLIC) action in the framework of Quenched Lattice QCD. Of particular interest is the determination of a negative quadrupole moment, indicating that the ρ meson is not spherically symmetric.

We present first results for the masses of positive and negative parity excited baryons calculated in lattice QCD using an O(a 2 )-improved gluon action and a fat-link irrelevant clover (FLIC) fermion action in which only the irrelevant operators are constructed with APE-smeared links. The results are in agreement with earlier calculations of N * resonances using improved actions and exhibit a clear mass splitting between the nucleon and its chiral partner. An correlation matrix analysis reveals two low-lying J P = 1 2 − states with a small mass splitting. The study of different Λ interpolating fields suggests a similar splitting between the lowest two Λ 1 2 − octet states. However, the empirical mass suppression of the Λ * (1405) is not evident in these quenched QCD simulations, suggesting a potentially important role for the meson cloud of the Λ * (1405) and/or a need for more exotic interpolating fields.

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Resolution and uncertainty in controlled-source electromagnetic (CSEM) inversion is most naturally approached using a Bayesian framework. Resolution can be inferred by hierarchical models with free parameters for effective correlation lengths (“Bayesian smoothing”), or model-choice frameworks applied to variable resolution spatial models (Bayesian splitting/merging). Typical 1D CSEM data can be modeled with quite parsimonious models, typically O(10) parameters per common midpoint. Efficient optimizations for the CSEM problem must address the challenges of poor scaling, strong nonlinearity, multimodality and the necessity of bound constraints. The posterior parameter uncertainties are frequently controlled by the nonlinearity, and linearized approaches to uncertainty usually are very poor. In Markov Chain Monte Carlo (MCMC) approaches, the nonlinearity and poor scaling make good mixing hard to achieve. A novel, approximate frequentist method we call the Bayesianized parametric bootstrap (sometimes called randomized maximum likelihood) is much more efficient than MCMC in this problem, considerably better than linearized analysis but tends to modestly overstate uncertainties. The software that implements these ideas for the 1D CSEM problem is made available under an open-source license agreement.