Measurement of the ttbar Production Cross Section in ppbar Collisions at sqrt(s)=1.96 TeV using Lepton + Jets Events with Lifetime b-tagging

We present a measurement of the top quark pair ($t\bar{t}$) production cross section ($\sigma_{t\bar{t}}$) in $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV using 230 pb$^{-1}$ of data collected by the D0 experiment at the Fermilab Tevatron Collider. We select events with one charged lepton (electron or muon), missing transverse energy, and jets in the final state. We employ lifetime-based b-jet identification techniques to further enhance the $t\bar{t}$ purity of the selected sample. For a top quark mass of 175 GeV, we measure $\sigma_{t\bar{t}}=8.6^{+1.6}_{-1.5}(stat.+syst.)\pm 0.6(lumi.)$ pb, in agreement with the standard model expectation.Comment: 7 pages, 2 figures, 3 tables Submitted to Phys.Rev.Let

Measurement of Semileptonic Branching Fractions of B Mesons to Narrow D** States

Using the data accumulated in 2002-2004 with the DO detector in proton-antiproton collisions at the Fermilab Tevatron collider with centre-of-mass energy 1.96 TeV, the branching fractions of the decays B -> \bar{D}_1^0(2420) \mu^+ \nu_\mu X and B -> \bar{D}_2^{*0}(2460) \mu^+ \nu_\mu X and their ratio have been measured: BR(\bar{b}->B) \cdot BR(B-> \bar{D}_1^0 \mu^+ \nu_\mu X) \cdot BR(\bar{D}_1^0 -> D*- pi+) = (0.087+-0.007(stat)+-0.014(syst))%; BR(\bar{b}->B)\cdot BR(B->D_2^{*0} \mu^+ \nu_\mu X) \cdot BR(\bar{D}_2^{*0} -> D*- \pi^+) = (0.035+-0.007(stat)+-0.008(syst))%; and (BR(B -> \bar{D}_2^{*0} \mu^+ \nu_\mu X)BR(D2*0->D*- pi+)) / (BR(B -> \bar{D}_1^{0} \mu^+ \nu_\mu X)\cdot BR(\bar{D}_1^{0}->D*- \pi^+)) = 0.39+-0.09(stat)+-0.12(syst), where the charge conjugated states are always implied.Comment: submitted to Phys. Rev. Let

Data assimilation in a system with two scales-combining two initialization techniques

11 pages, 11 figures, 1 tableFull-text version available Open Access at: http://clivar.iim.csic.es/?q=es/node/319An ensemble Kalman filter (EnKF) is used to assimilate data onto a non-linear chaotic model, coupling two kinds of variables. The first kind of variables of the system is characterized as large amplitude, slow, large scale, distributed in eight equally spaced locations around a circle. The second kind of variables are small amplitude, fast, and short scale, distributed in 256 equally spaced locations. Synthetic observations are obtained from the model and the observational error is proportional to their respective amplitudes. The performance of the EnKF is affected by differences in the spatial correlation scales of the variables being assimilated. This method allows the simultaneous assimilation of all the variables. The ensemble filter also allows assimilating only the large-scale variables, letting the small-scale variables to freely evolve. Assimilation of the large-scale variables together with a few small-scale variables significantly degrades the filter. These results are explained by the spurious correlations that arise from the sampled ensemble covariances. An alternative approach is to combine two different initialization techniques for the slow and fast variables. Here, the fast variables are initialized by restraining the evolution of the ensemble members, using a Newtonian relaxation toward the observed fast variables. Then, the usual ensemble analysis is used to assimilate the large-scale observationsThis study is supported by the Spanish National Science Program under contracts ESP2005–06823-C05 and ESP2007–65667-C04Peer reviewe

Building consensus around the assessment and interpretation of Symbiodiniaceae diversity.

Within microeukaryotes, genetic variation and functional variation sometimes accumulate more quickly than morphological differences. To understand the evolutionary history and ecology of such lineages, it is key to examine diversity at multiple levels of organization. In the dinoflagellate family Symbiodiniaceae, which can form endosymbioses with cnidarians (e.g., corals, octocorals, sea anemones, jellyfish), other marine invertebrates (e.g., sponges, molluscs, flatworms), and protists (e.g., foraminifera), molecular data have been used extensively over the past three decades to describe phenotypes and to make evolutionary and ecological inferences. Despite advances in Symbiodiniaceae genomics, a lack of consensus among researchers with respect to interpreting genetic data has slowed progress in the field and acted as a barrier to reconciling observations. Here, we identify key challenges regarding the assessment and interpretation of Symbiodiniaceae genetic diversity across three levels: species, populations, and communities. We summarize areas of agreement and highlight techniques and approaches that are broadly accepted. In areas where debate remains, we identify unresolved issues and discuss technologies and approaches that can help to fill knowledge gaps related to genetic and phenotypic diversity. We also discuss ways to stimulate progress, in particular by fostering a more inclusive and collaborative research community. We hope that this perspective will inspire and accelerate coral reef science by serving as a resource to those designing experiments, publishing research, and applying for funding related to Symbiodiniaceae and their symbiotic partnerships