727 research outputs found
On the use of the ratio of small to large separations in asteroseismic model fitting
Context. The use of ratios of small to large separations as a diagnostic of
stellar interiors. Aims. To demonstrate that model fitting by comparing
observed and model separation ratios at the same n values is in error, and to
present a correct procedure. Methods. Theoretical analysis using phase shifts
and numerical models. Results. We show that the separation ratios of stellar
models with the same interior structure, but different outer layers, are not
the same when compared at the same n values, but are the same when evaluated at
the same frequencies by interpolation. The separation ratios trace the phase
shift differences as a function of frequency not of n. We give examples from
model fitting where the ratios at the same n values agree within the error
estimates, but do not agree when evaluated at the same frequencies and the
models do not have the same interior structure. The correct procedure is to
compare observed ratios with those of models interpolated to the observed
frequencies.Comment: 7 pages, 14 figures, 3 table
Domain Walls in Superfluid 3He-B
We consider domain walls between regions of superfluid 3He-B in which one
component of the order parameter has the opposite sign in the two regions far
from one another. We report calculations of the order parameter profile and the
free energy for two types of domain wall, and discuss how these structures are
relevant to superfluid 3He confined between two surfaces.Comment: 6 pages with 3 figures. Conference proceedings of QSF 2004, Trento,
Ital
Specific heat jump at superconducting transition in the presence of Spin-Density-Wave in iron-pnictides
We analyze the magnitude of the specific heat jump \Delta C at the
superconducting transition temperature T_c in the situation when
superconductivity develops in the pre-existing antiferromagnetic phase. We show
that \Delta C/T_c differs from the BCS value and is peaked at the tri-critical
point where this coexistence phase first emerges. Deeper in the magnetic phase,
the onset of coexistence, T_c, drops and \Delta C/T_c decreases, roughly as
\Delta C/T_c \propto T^2_c at intermediate T_c and exponentially at the lowest
T_c, in agreement with the observed behavior of \Delta C/T_c in iron-based
superconductors.Comment: 4+ pages, 3 figure
Theory of thermal conductivity in extended- state superconductors: application to ferropnictides
Within a two-band model for the recently discovered ferropnictide materials,
we calculate the thermal conductivity assuming general superconducting states
of ("s-wave") symmetry, considering both currently popular isotropic
"sign-changing" states and states with strong anisotropy, including those
which manifest nodes or deep minima of the order parameter. We consider both
intra- and interband disorder scattering effects, and show that in situations
where a low-temperature linear- exists in the thermal conductivity, it is
not always "universal" as in d-wave superconductors. We discuss the conditions
under which such a term can disappear, as well as how it can be induced by a
magnetic field. We compare our results to several recent experiments.Comment: 13 page
Antiferromagnetic order in CeCoIn5 oriented by spin-orbital coupling
An incommensurate spin density wave ( phase) confined inside the
superconducting state at high basal plane magnetic field is an unique property
of the heavy fermion metal CeCoIn. The neutron scattering experiments and
the theoretical studies point out that this state come out from the soft mode
condensation of magnetic resonance excitations. We show that the fixation of
direction of antiferromagnetic modulations by a magnetic field reported by
Gerber et al., Nat. Phys. {\bf 10}, 126 (2014) is explained by spin-orbit
coupling. This result, obtained on the basis of quite general phenomenological
arguments, is supported by the microscopic derivation of the
susceptibility dependence on the mutual orientation of the basal plane magnetic
field and the direction of modulation of spin polarization in a multi-band
metal.Comment: 7 pages plus 2 pages with 2 figure
Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric turbulence theoretical models and to evaluate them against the experimentally measured data
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