3,461 research outputs found
Anomalies in the Entanglement Properties of the Square Lattice Heisenberg Model
We compute the bipartite entanglement properties of the spin-half
square-lattice Heisenberg model by a variety of numerical techniques that
include valence bond quantum Monte Carlo (QMC), stochastic series expansion
QMC, high temperature series expansions and zero temperature coupling constant
expansions around the Ising limit. We find that the area law is always
satisfied, but in addition to the entanglement entropy per unit boundary
length, there are other terms that depend logarithmically on the subregion
size, arising from broken symmetry in the bulk and from the existence of
corners at the boundary. We find that the numerical results are anomalous in
several ways. First, the bulk term arising from broken symmetry deviates from
an exact calculation that can be done for a mean-field Neel state. Second, the
corner logs do not agree with the known results for non-interacting Boson
modes. And, third, even the finite temperature mutual information shows an
anomalous behavior as T goes to zero, suggesting that T->0 and L->infinity
limits do not commute. These calculations show that entanglement entropy
demonstrates a very rich behavior in d>1, which deserves further attention.Comment: 12 pages, 7 figures, 2 tables. Numerical values in Table I correcte
Quasi-adiabatic Continuation of Quantum States: The Stability of Topological Ground State Degeneracy and Emergent Gauge Invariance
We define for quantum many-body systems a quasi-adiabatic continuation of
quantum states. The continuation is valid when the Hamiltonian has a gap, or
else has a sufficiently small low-energy density of states, and thus is away
from a quantum phase transition. This continuation takes local operators into
local operators, while approximately preserving the ground state expectation
values. We apply this continuation to the problem of gauge theories coupled to
matter, and propose a new distinction, perimeter law versus "zero law" to
identify confinement. We also apply the continuation to local bosonic models
with emergent gauge theories. We show that local gauge invariance is
topological and cannot be broken by any local perturbations in the bosonic
models in either continuous or discrete gauge groups. We show that the ground
state degeneracy in emergent discrete gauge theories is a robust property of
the bosonic model, and we argue that the robustness of local gauge invariance
in the continuous case protects the gapless gauge boson.Comment: 15 pages, 6 figure
Bose Glass in Large N Commensurate Dirty Boson Model
The large N commensurate dirty boson model, in both the weakly and strongly
commensurate cases, is considered via a perturbative renormalization group
treatment. In the weakly commensurate case, there exists a fixed line under RG
flow, with varying amounts of disorder along the line. Including 1/N
corrections causes the system to flow to strong disorder, indicating that the
model does not have a phase transition perturbatively connected to the Mott
Insulator-Superfluid (MI-SF) transition. I discuss the qualitative effects of
instantons on the low energy density of excitations. In the strongly
commensurate case, a fixed point found previously is considered and results are
obtained for higher moments of the correlation functions. To lowest order,
correlation functions have a log-normal distribution. Finally, I prove two
interesting theorems for large N vector models with disorder, relevant to the
problem of replica symmetry breaking and frustration in such systems.Comment: 16 pages, 7 figure
LISA Data Analysis using MCMC methods
The Laser Interferometer Space Antenna (LISA) is expected to simultaneously
detect many thousands of low frequency gravitational wave signals. This
presents a data analysis challenge that is very different to the one
encountered in ground based gravitational wave astronomy. LISA data analysis
requires the identification of individual signals from a data stream containing
an unknown number of overlapping signals. Because of the signal overlaps, a
global fit to all the signals has to be performed in order to avoid biasing the
solution. However, performing such a global fit requires the exploration of an
enormous parameter space with a dimension upwards of 50,000. Markov Chain Monte
Carlo (MCMC) methods offer a very promising solution to the LISA data analysis
problem. MCMC algorithms are able to efficiently explore large parameter
spaces, simultaneously providing parameter estimates, error analyses and even
model selection. Here we present the first application of MCMC methods to
simulated LISA data and demonstrate the great potential of the MCMC approach.
Our implementation uses a generalized F-statistic to evaluate the likelihoods,
and simulated annealing to speed convergence of the Markov chains. As a final
step we super-cool the chains to extract maximum likelihood estimates, and
estimates of the Bayes factors for competing models. We find that the MCMC
approach is able to correctly identify the number of signals present, extract
the source parameters, and return error estimates consistent with Fisher
information matrix predictions.Comment: 14 pages, 7 figure
Evolutionary game theory in growing populations
Existing theoretical models of evolution focus on the relative fitness
advantages of different mutants in a population while the dynamic behavior of
the population size is mostly left unconsidered. We here present a generic
stochastic model which combines the growth dynamics of the population and its
internal evolution. Our model thereby accounts for the fact that both
evolutionary and growth dynamics are based on individual reproduction events
and hence are highly coupled and stochastic in nature. We exemplify our
approach by studying the dilemma of cooperation in growing populations and show
that genuinely stochastic events can ease the dilemma by leading to a transient
but robust increase in cooperationComment: 4 pages, 2 figures and 2 pages supplementary informatio
Characterizing the Gravitational Wave Signature from Cosmic String Cusps
Cosmic strings are predicted to form kinks and cusps that travel along the
string at close to the speed of light. These disturbances are radiated away as
highly beamed gravitational waves that produce a burst like pulse as the cone
of emission sweeps past an observer. Gravitational wave detectors such as the
Laser Interferometer Space Antenna (LISA) and the Laser Interferometer
Gravitational wave Observatory (LIGO) will be capable of detecting these bursts
for a wide class of string models. Such a detection would illuminate the fields
of string theory, cosmology, and relativity. Here we develop template based
Markov Chain Monte Carlo (MCMC) techniques that can efficiently detect and
characterize the signals from cosmic string cusps. We estimate how well the
signal parameters can be recovered by the advanced LIGO-Virgo network and the
LISA detector using a combination of MCMC and Fisher matrix techniques. We also
consider joint detections by the ground and space based instruments. We show
that a parallel tempered MCMC approach can detect and characterize the signals
from cosmic string cusps, and we demonstrate the utility of this approach on
simulated data from the third round of Mock LISA Data Challenges (MLDCs).Comment: 10 pages, 10 figure
The statistical mechanics of complex signaling networks : nerve growth factor signaling
It is becoming increasingly appreciated that the signal transduction systems
used by eukaryotic cells to achieve a variety of essential responses represent
highly complex networks rather than simple linear pathways. While significant
effort is being made to experimentally measure the rate constants for
individual steps in these signaling networks, many of the parameters required
to describe the behavior of these systems remain unknown, or at best,
estimates. With these goals and caveats in mind, we use methods of statistical
mechanics to extract useful predictions for complex cellular signaling
networks. To establish the usefulness of our approach, we have applied our
methods towards modeling the nerve growth factor (NGF)-induced differentiation
of neuronal cells. Using our approach, we are able to extract predictions that
are highly specific and accurate, thereby enabling us to predict the influence
of specific signaling modules in determining the integrated cellular response
to the two growth factors. We show that extracting biologically relevant
predictions from complex signaling models appears to be possible even in the
absence of measurements of all the individual rate constants. Our methods also
raise some interesting insights into the design and possible evolution of
cellular systems, highlighting an inherent property of these systems wherein
particular ''soft'' combinations of parameters can be varied over wide ranges
without impacting the final output and demonstrating that a few ''stiff''
parameter combinations center around the paramount regulatory steps of the
network. We refer to this property -- which is distinct from robustness -- as
''sloppiness.''Comment: 24 pages, 10 EPS figures, 1 GIF (makes 5 multi-panel figs + caption
for GIF), IOP style; supp. info/figs. included as brown_supp.pd
Criticality, the area law, and the computational power of PEPS
The projected entangled pair state (PEPS) representation of quantum states on
two-dimensional lattices induces an entanglement based hierarchy in state
space. We show that the lowest levels of this hierarchy exhibit an enormously
rich structure including states with critical and topological properties as
well as resonating valence bond states. We prove, in particular, that coherent
versions of thermal states of any local 2D classical spin model correspond to
such PEPS, which are in turn ground states of local 2D quantum Hamiltonians.
This correspondence maps thermal onto quantum fluctuations, and it allows us to
analytically construct critical quantum models exhibiting a strict area law
scaling of the entanglement entropy in the face of power law decaying
correlations. Moreover, it enables us to show that there exist PEPS within the
same class as the cluster state, which can serve as computational resources for
the solution of NP-hard problems
Branching of the Falkner-Skan solutions for λ < 0
The Falkner-Skan equation f'" + ff" + λ(1 - f'^2) = 0, f(0) = f'(0) = 0, is discussed for λ < 0. Two types of problems, one with f'(∞) = 1 and another with f'(∞) = -1, are considered. For λ = 0- a close relation between these two types is found. For λ < -1 both types of problem allow multiple solutions which may be distinguished by an integer N denoting the number of zeros of f' - 1. The numerical results indicate that the solution branches with f'(∞) = 1 and those with f'(∞) = -1 tend towards a common limit curve as N increases indefinitely. Finally a periodic solution, existing for λ < -1, is presented.
Diffusion Processes on Small-World Networks with Distance-Dependent Random-Links
We considered diffusion-driven processes on small-world networks with
distance-dependent random links. The study of diffusion on such networks is
motivated by transport on randomly folded polymer chains, synchronization
problems in task-completion networks, and gradient driven transport on
networks. Changing the parameters of the distance-dependence, we found a rich
phase diagram, with different transient and recurrent phases in the context of
random walks on networks. We performed the calculations in two limiting cases:
in the annealed case, where the rearrangement of the random links is fast, and
in the quenched case, where the link rearrangement is slow compared to the
motion of the random walker or the surface. It has been well-established that
in a large class of interacting systems, adding an arbitrarily small density
of, possibly long-range, quenched random links to a regular lattice interaction
topology, will give rise to mean-field (or annealed) like behavior. In some
cases, however, mean-field scaling breaks down, such as in diffusion or in the
Edwards-Wilkinson process in "low-dimensional" small-world networks. This
break-down can be understood by treating the random links perturbatively, where
the mean-field (or annealed) prediction appears as the lowest-order term of a
naive perturbation expansion. The asymptotic analytic results are also
confirmed numerically by employing exact numerical diagonalization of the
network Laplacian. Further, we construct a finite-size scaling framework for
the relevant observables, capturing the cross-over behaviors in finite
networks. This work provides a detailed account of the
self-consistent-perturbative and renormalization approaches briefly introduced
in two earlier short reports.Comment: 36 pages, 27 figures. Minor revisions in response to the referee's
comments. Furthermore, some typos were fixed and new references were adde
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