16 research outputs found
Persistence in nonequilibrium surface growth
Persistence probabilities of the interface height in (1+1)- and
(2+1)-dimensional atomistic, solid-on-solid, stochastic models of surface
growth are studied using kinetic Monte Carlo simulations, with emphasis on
models that belong to the molecular beam epitaxy (MBE) universality class. Both
the initial transient and the long-time steady-state regimes are investigated.
We show that for growth models in the MBE universality class, the nonlinearity
of the underlying dynamical equation is clearly reflected in the difference
between the measured values of the positive and negative persistence exponents
in both transient and steady-state regimes. For the MBE universality class, the
positive and negative persistence exponents in the steady-state are found to be
and ,
respectively, in (1+1) dimensions, and and
, respectively, in (2+1) dimensions. The noise
reduction technique is applied on some of the (1+1)-dimensional models in order
to obtain accurate values of the persistence exponents. We show analytically
that a relation between the steady-state persistence exponent and the dynamic
growth exponent, found earlier to be valid for linear models, should be
satisfied by the smaller of the two steady-state persistence exponents in the
nonlinear models. Our numerical results for the persistence exponents are
consistent with this prediction. We also find that the steady-state persistence
exponents can be obtained from simulations over times that are much shorter
than that required for the interface to reach the steady state. The dependence
of the persistence probability on the system size and the sampling time is
shown to be described by a simple scaling form.Comment: 28 pages, 16 figure
Optical Magnetometry
Some of the most sensitive methods of measuring magnetic fields utilize
interactions of resonant light with atomic vapor. Recent developments in this
vibrant field are improving magnetometers in many traditional areas such as
measurement of geomagnetic anomalies and magnetic fields in space, and are
opening the door to new ones, including, dynamical measurements of bio-magnetic
fields, detection of nuclear magnetic resonance (NMR), magnetic-resonance
imaging (MRI), inertial-rotation sensing, magnetic microscopy with cold atoms,
and tests of fundamental symmetries of Nature.Comment: 11 pages; 4 figures; submitted to Nature Physic
Persistence in nonequilibrium surface growth
Persistence probabilities of the interface height in (1 + 1)- and (2 + 1)-dimensional atomistic, solid-on-solid, stochastic models of surface growth are studied using kinetic Monte Carlo simulations, with emphasis on models that belong to the molecular beam epitaxy (MBE) universality class. Both the initial transient and the long-time steady-state regimes are investigated. We show that for growth models in the MBE universality class, the nonlinearity of the underlying dynamical equation is clearly reflected in the difference between the measured values of the positive and negative persistence exponents in both transient and steady-state regimes. For the MBE universality class, the positive and negative persistence exponents in the steady-state are found to be = 0.66±0.02 and = 0.78±0.02, respectively, in (1 + 1) dimensions, and = 0.76±0.02 and = 0.85±0.02, respectively, in (2 + 1) dimensions. The noise reduction technique is applied on some of the (1 + 1)-dimensional models in order to obtain accurate values of the persistence exponents. We show analytically that a relation between the steady-state persistence exponent and the dynamic growth exponent, found earlier to be valid for linear models, should be satisfied by the smaller of the two steady-state persistence exponents in the nonlinear models. Our numerical results for the persistence exponents are consistent with this prediction. We also find that the steady-state persistence exponents can be obtained from simulations over times that are much shorter than that required for the interface to reach the steady state. The dependence of the persistence probability on the system size and the sampling time is shown to be described by a simple scaling form