10,659 research outputs found
Protoplanetary Disk Turbulence Driven by the Streaming Instability: Non-Linear Saturation and Particle Concentration
We present simulations of the non-linear evolution of streaming instabilities
in protoplanetary disks. The two components of the disk, gas treated with grid
hydrodynamics and solids treated as superparticles, are mutually coupled by
drag forces. We find that the initially laminar equilibrium flow spontaneously
develops into turbulence in our unstratified local model. Marginally coupled
solids (that couple to the gas on a Keplerian time-scale) trigger an upward
cascade to large particle clumps with peak overdensities above 100. The clumps
evolve dynamically by losing material downstream to the radial drift flow while
receiving recycled material from upstream. Smaller, more tightly coupled solids
produce weaker turbulence with more transient overdensities on smaller length
scales. The net inward radial drift is decreased for marginally coupled
particles, whereas the tightly coupled particles migrate faster in the
saturated turbulent state. The turbulent diffusion of solid particles, measured
by their random walk, depends strongly on their stopping time and on the
solids-to-gas ratio of the background state, but diffusion is generally modest,
particularly for tightly coupled solids. Angular momentum transport is too weak
and of the wrong sign to influence stellar accretion. Self-gravity and
collisions will be needed to determine the relevance of particle overdensities
for planetesimal formation.Comment: Accepted for publication in ApJ (17 pages). Movies of the simulations
can be downloaded at http://www.mpia.de/~johansen/research_en.ph
Stochastics theory of log-periodic patterns
We introduce an analytical model based on birth-death clustering processes to
help understanding the empirical log-periodic corrections to power-law scaling
and the finite-time singularity as reported in several domains including
rupture, earthquakes, world population and financial systems. In our
stochastics theory log-periodicities are a consequence of transient clusters
induced by an entropy-like term that may reflect the amount of cooperative
information carried by the state of a large system of different species. The
clustering completion rates for the system are assumed to be given by a simple
linear death process. The singularity at t_{o} is derived in terms of
birth-death clustering coefficients.Comment: LaTeX, 1 ps figure - To appear J. Phys. A: Math & Ge
Three-body properties of low-lying Be resonances
We compute the three-body structure of the lowest resonances of Be
considered as two neutrons around an inert Be core. This is an extension
of the bound state calculations of Be into the continuum spectrum. We
investigate the lowest resonances of angular momenta and parities, ,
and . Surprisingly enough, they all are naturally occurring in
the three-body model. We calculate bulk structure dominated by small distance
properties as well as decays determined by the asymptotic large-distance
structure. Both and have two-body Be-neutron d-wave
structure, while has an even mixture of and d-waves. The
corresponding relative neutron-neutron partial waves are distributed among ,
, and d-waves. The branching ratios show different mixtures of one-neutron
emission, three-body direct, and sequential decays. We argue for spin and
parities, , and , to the resonances at 0.89, 2.03, 5.13,
respectively. The computed structures are in agreement with existing reaction
measurements.Comment: To be published in Physical Review
A new approach to the inverse problem for current mapping in thin-film superconductors
A novel mathematical approach has been developed to complete the inversion of
the Biot-Savart law in one- and two-dimensional cases from measurements of the
perpendicular component of the magnetic field using the well-developed
Magneto-Optical Imaging technique. Our approach, especially in the 2D case, is
provided in great detail to allow a straightforward implementation as opposed
to those found in the literature. Our new approach also refines our previous
results for the 1D case [Johansen et al., Phys. Rev. B 54, 16264 (1996)], and
streamlines the method developed by Jooss et al. [Physica C 299, 215 (1998)]
deemed as the most accurate if compared to that of Roth et al. [J. Appl. Phys.
65, 361 (1989)]. We also verify and streamline the iterative technique, which
was developed following Laviano et al. [Supercond. Sci. Technol. 16, 71 (2002)]
to account for in-plane magnetic fields caused by the bending of the applied
magnetic field due to the demagnetising effect. After testing on
magneto-optical images of a high quality YBa2Cu3O7 superconducting thin film,
we show that the procedure employed is effective
Classification of Possible Finite-Time Singularities by Functional Renormalization
Starting from a representation of the early time evolution of a dynamical
system in terms of the polynomial expression of some observable f (t) as a
function of the time variable in some interval 0 < t < T, we investigate how to
extrapolate/forecast in some optimal stability sense the future evolution of
f(t) for time t>T. Using the functional renormalization of Yukalov and Gluzman,
we offer a general classification of the possible regimes that can be defined
based on the sole knowledge of the coefficients of a second-order polynomial
representation of the dynamics. In particular, we investigate the conditions
for the occurence of finite-time singularities from the structure of the time
series, and quantify the critical time and the functional nature of the
singularity when present. We also describe the regimes when a smooth extremum
replaces the singularity and determine its position and amplitude. This extends
previous works by (1) quantifying the stability of the functional
renormalization method more accurately, (2) introducing new global constraints
in terms of moments and (3) going beyond the ``mean-field'' approximation.Comment: Latex document of 18 pages + 7 ps figure
Log-periodic route to fractal functions
Log-periodic oscillations have been found to decorate the usual power law
behavior found to describe the approach to a critical point, when the
continuous scale-invariance symmetry is partially broken into a discrete-scale
invariance (DSI) symmetry. We classify the `Weierstrass-type'' solutions of the
renormalization group equation F(x)= g(x)+(1/m)F(g x) into two classes
characterized by the amplitudes A(n) of the power law series expansion. These
two classes are separated by a novel ``critical'' point. Growth processes
(DLA), rupture, earthquake and financial crashes seem to be characterized by
oscillatory or bounded regular microscopic functions g(x) that lead to a slow
power law decay of A(n), giving strong log-periodic amplitudes. In contrast,
the regular function g(x) of statistical physics models with
``ferromagnetic''-type interactions at equibrium involves unbound logarithms of
polynomials of the control variable that lead to a fast exponential decay of
A(n) giving weak log-periodic amplitudes and smoothed observables. These two
classes of behavior can be traced back to the existence or abscence of
``antiferromagnetic'' or ``dipolar''-type interactions which, when present,
make the Green functions non-monotonous oscillatory and favor spatial modulated
patterns.Comment: Latex document of 29 pages + 20 ps figures, addition of a new
demonstration of the source of strong log-periodicity and of a justification
of the general offered classification, update of reference lis
Dynamic filtering of static dipoles in magnetoencephalography
We consider the problem of estimating neural activity from measurements
of the magnetic fields recorded by magnetoencephalography. We exploit
the temporal structure of the problem and model the neural current as a
collection of evolving current dipoles, which appear and disappear, but whose
locations are constant throughout their lifetime. This fully reflects the physiological
interpretation of the model.
In order to conduct inference under this proposed model, it was necessary
to develop an algorithm based around state-of-the-art sequential Monte
Carlo methods employing carefully designed importance distributions. Previous
work employed a bootstrap filter and an artificial dynamic structure
where dipoles performed a random walk in space, yielding nonphysical artefacts
in the reconstructions; such artefacts are not observed when using the
proposed model. The algorithm is validated with simulated data, in which
it provided an average localisation error which is approximately half that of
the bootstrap filter. An application to complex real data derived from a somatosensory
experiment is presented. Assessment of model fit via marginal
likelihood showed a clear preference for the proposed model and the associated
reconstructions show better localisation
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