Radion Phenomenology in Realistic Warped Space Models

We investigate the phenomenology of the Randall-Sundrum radion in realistic models of electroweak symmetry breaking with bulk gauge and fermion fields, since the radion may turn out to be the lightest particle in such models. We calculate the coupling of the radion in such scenarios to bulk fermion and gauge modes. Special attention needs to be devoted to the coupling to massless gauge fields (photon, gluon), since it is well known that loop effects may be important for these fields. We also present a detailed explanation of these couplings from the CFT interpretation. We then use these couplings to determine the radion branching fractions and discuss some of the discovery potential of the LHC for the radion. We find that the gamma-gamma signal is enhanced over most of the range of the radion mass over the gamma-gamma signal of a SM Higgs, as long as the RS scale is sufficiently low. However, the signal significance depends strongly on free parameters that characterize the magnitude of bare brane-localized kinetic terms for the massless gauge fields. In the absence of such terms, the signal can be be enhanced over the traditional RS1 models (where all standard model fields are localized on the IR brane), but the signal can also be reduced compared to RS1 if the brane localized terms are sizeable. We also show that for larger radion masses, where the gamma-gamma signal is no longer significant, one can use the usual 4 lepton signal to discover the radion.Comment: 28 pages, 7 figure

Genome-wide inference of ancestral recombination graphs

The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of n chromosomes conditional on an ARG of n-1 chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the true posterior distribution and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. Preliminary results also indicate that our methods can be used to gain insight into complex features of human population structure, even with a noninformative prior distribution.Comment: 88 pages, 7 main figures, 22 supplementary figures. This version contains a substantially expanded genomic data analysi

SWEEPFINDER2: Increased sensitivity, robustness, and flexibility

SweepFinder is a popular program that implements a powerful likelihood-based method for detecting recent positive selection, or selective sweeps. Here, we present SweepFinder2, an extension of SweepFinder with increased sensitivity and robustness to the confounding effects of mutation rate variation and background selection, as well as increased flexibility that enables the user to examine genomic regions in greater detail and to specify a fixed distance between test sites. Moreover, SweepFinder2 enables the use of invariant sites for sweep detection, increasing both its power and precision relative to SweepFinder

Top quark chromomagnetic dipole moment in the littlest Higgs model with T-parity

The littlest Higgs model with T-parity, which is called $LHT$ model, predicts the existence of the new particles, such as heavy top quark, heavy gauge bosons, and mirror fermions. We calculate the one-loop contributions of these new particles to the top quark chromomagnetic dipole moment $(CMDM)$ $\Delta K$. We find that the contribution of the $LHT$ model is one order of magnitude smaller than the standard model prediction value.Comment: latex files, 12 pages, 3 figure

Radiative decay Z_H-> \gamma A_H in the little Higgs model with T-parity

In the little Higgs model with T-parity (LHTM), the only tree-level kinematically allowed two-body decay of the Z_H boson is Z_H-> A_H H and thus one-loop induced two-body decays may have a significant rate. We study the Z_H-> \gamma A_H decay, which is induced at the one-loop level by a fermion triangle and is interesting as it depends on the mechanism of anomaly cancellation of the model. All the relevant two- and three-body decays of the Z_H gauge boson arising at the tree-level are also calculated. We consider a small region of the parameter space where the scale of the symmetry breaking f is still allowed to be as low as 500 GeV by electroweak precision data. We first analyze the scenario of a Higgs boson with a mass of 120 GeV. We found that the Z_H->\gamma A_H branching ratio can be of the order of a tree-level three-body decay and may be at the reach of detection at the LHC for f close to 500 GeV, but it may be difficult to detect for f=1 TeV. There is also an scenario where the Higgs boson has an intermediate mass such that the Z_H-> A_H H decay is closed, the Z_H-> \gamma A_H gets considerably enhanced and the chances of detection get a large boost.Comment: 19 pages, 9 figures, 2 table

Error and Error Mitigation in Low-Coverage Genome Assemblies

The recent release of twenty-two new genome sequences has dramatically increased the data available for mammalian comparative genomics, but twenty of these new sequences are currently limited to ~2× coverage. Here we examine the extent of sequencing error in these 2× assemblies, and its potential impact in downstream analyses. By comparing 2× assemblies with high-quality sequences from the ENCODE regions, we estimate the rate of sequencing error to be 1–4 errors per kilobase. While this error rate is fairly modest, sequencing error can still have surprising effects. For example, an apparent lineage-specific insertion in a coding region is more likely to reflect sequencing error than a true biological event, and the length distribution of coding indels is strongly distorted by error. We find that most errors are contributed by a small fraction of bases with low quality scores, in particular, by the ends of reads in regions of single-read coverage in the assembly. We explore several approaches for automatic sequencing error mitigation (SEM), making use of the localized nature of sequencing error, the fact that it is well predicted by quality scores, and information about errors that comes from comparisons across species. Our automatic methods for error mitigation cannot replace the need for additional sequencing, but they do allow substantial fractions of errors to be masked or eliminated at the cost of modest amounts of over-correction, and they can reduce the impact of error in downstream phylogenomic analyses. Our error-mitigated alignments are available for download.National Science Foundation (U.S.) (Faculty Early Career Development grant DBI-0644111)National Science Foundation (U.S.) (Faculty Early Career Development grant DBI-0644282)National Science Foundation (U.S.) (Faculty Early Career Development grant U54 HG004555-01)David & Lucile Packard FoundationDavid & Lucile Packard Foundation (Fellowship for Science and Engineering