681 research outputs found
The decoupling limit of Multi-Gravity: Multi-Galileons, Dualities and More
In this paper we investigate the decoupling limit of a particular class of
multi-gravity theories, i.e. of theories of interacting spin-2 fields. We
explicitly compute the interactions of helicity-0 modes in this limit, showing
that they take on the form of multi-Galileons and dual forms. In the process we
extend the recently discovered Galileon dualities, deriving a set of new
multi-Galileon dualities. These are also intrinsically connected to healthy,
but higher-derivative, multi-scalar field theories akin to `beyond Horndeski'
models.Comment: 41 pages, 2 figure
Strong-coupling scales and the graph structure of multi-gravity theories
In this paper we consider how the strong-coupling scale, or perturbative
cutoff, in a multi-gravity theory depends upon the presence and structure of
interactions between the different fields. This can elegantly be rephrased in
terms of the size and structure of the `theory graph' which depicts the
interactions in a given theory. We show that the question can be answered in
terms of the properties of various graph-theoretical matrices, affording an
efficient way to estimate and place bounds on the strong-coupling scale of a
given theory. In light of this we also consider the problem of relating a given
theory graph to a discretised higher dimensional theory, a la dimensional
deconstruction.Comment: 23 pages, 7 figures; v2: additional references included, and minor
typos corrected; version published in JHE
Cycles of interactions in multi-gravity theories
In this paper we study multi-gravity (multi-metric and multi-vielbein)
theories in the presence of cycles of interactions (cycles in the so-called
`theory graph'). It has been conjectured that in multi-metric theories such
cycles lead to the introduction of a ghost-like instability, which, however, is
absent in the multi-vielbein version of such theories. In this paper we answer
this question in the affirmative by explicitly demonstrating the presence of
the ghost in such multi-metric theories in the form of dangerous higher
derivative terms in the decoupling limit Lagrangian; we also explain why these
terms are absent in the vielbein version of these theories. Finally we discuss
the ramifications of our result on the dimensional deconstruction paradigm,
which would seek an equivalence between such theories and a truncated
Kaluza-Klein theory, and find that the impediment to taking the continuum limit
due to a low strong-coupling scale is exacerbated by the presence of the ghost,
when these theories are constructed using metrics.Comment: 25 pages; v2: corrected an error in section 5.3.1 which changes
slightly the conclusions of that subsection; expanded section 6.1 to include
derivation of the scaling of the cutoff; version published in JHE
Understanding the errors of SHAPE-directed RNA structure modeling
Single-nucleotide-resolution chemical mapping for structured RNA is being
rapidly advanced by new chemistries, faster readouts, and coupling to
computational algorithms. Recent tests have shown that selective 2'-hydroxyl
acylation by primer extension (SHAPE) can give near-zero error rates (0-2%) in
modeling the helices of RNA secondary structure. Here, we benchmark the method
using six molecules for which crystallographic data are available: tRNA(phe)
and 5S rRNA from Escherichia coli, the P4-P6 domain of the Tetrahymena group I
ribozyme, and ligand-bound domains from riboswitches for adenine, cyclic
di-GMP, and glycine. SHAPE-directed modeling of these highly structured RNAs
gave an overall false negative rate (FNR) of 17% and a false discovery rate
(FDR) of 21%, with at least one helix prediction error in five of the six
cases. Extensive variations of data processing, normalization, and modeling
parameters did not significantly mitigate modeling errors. Only one varation,
filtering out data collected with deoxyinosine triphosphate during primer
extension, gave a modest improvement (FNR = 12%, and FDR = 14%). The residual
structure modeling errors are explained by the insufficient information content
of these RNAs' SHAPE data, as evaluated by a nonparametric bootstrapping
analysis. Beyond these benchmark cases, bootstrapping suggests a low level of
confidence (<50%) in the majority of helices in a previously proposed
SHAPE-directed model for the HIV-1 RNA genome. Thus, SHAPE-directed RNA
modeling is not always unambiguous, and helix-by-helix confidence estimates, as
described herein, may be critical for interpreting results from this powerful
methodology.Comment: Biochemistry, Article ASAP (Aug. 15, 2011
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