533 research outputs found
Stability and magnetic properties of Fe double-layers on Ir (111)
We investigate the interplay between the structural reconstruction and the
magnetic properties of Fe doublelayers on Ir (111)-substrate using
first-principles calculations based on density functional theory and mapping of
the total energies on an atomistic spin model. We show that, if a second Fe
monolayer is deposited on Fe/Ir (111), the stacking may change from hexagonal
close-packed to bcc (110)-like accompanied by a reduction of symmetry from
trigonal to centered rectangular. Although the bcc-like surface has a lower
coordination, we find that this is the structural ground state. This
reconstruction has a major impact on the magnetic structure. We investigate in
detail the changes in the magnetic exchange interaction, the magnetocrystalline
anisotropy, and the Dzyaloshinskii Moriya interaction depending on the stacking
sequence of the Fe double-layer. Based on our findings, we suggest a new
technique to engineer Dzyaloshinskii Moriya interactions in multilayer systems
employing symmetry considerations. The resulting anisotropic
Dzyaloshinskii-Moriya interactions may stabilize higher-order skyrmions or
antiskyrmions
Revealing the correlation between real-space structure and chiral magnetic order at the atomic scale
We image simultaneously the geometric, electronic and magnetic structure of a
buckled iron bilayer film that exhibits chiral magnetic order. We achieve this
by combining spin-polarized scanning tunneling microscopy and magnetic exchange
force microscopy (SPEX), to independently characterize the geometric as well as
the electronic and magnetic structure of non-flat surfaces. This new SPEX
imaging technique reveals the geometric height corrugation of the
reconstruction lines resulting from strong strain relaxation in the bilayer,
enabling the decomposition of the real-space from the eletronic structure at
the atomic level, and the correlation with the resultant spin spiral ground
state. By additionally utilizing adatom manipulation, we reveal the chiral
magnetic ground state of portions of the unit cell that were not previously
imaged with SP-STM alone. Using density functional theory (DFT), we investigate
the structural and electronic properties of the reconstructed bilayer and
identify the favorable stoichiometry regime in agreement with our experimental
result
Linear inverse problems with noise: primal and primal-dual splitting
In this paper, we propose two algorithms for solving linear inverse problems
when the observations are corrupted by noise. A proper data fidelity term
(log-likelihood) is introduced to reflect the statistics of the noise (e.g.
Gaussian, Poisson). On the other hand, as a prior, the images to restore are
assumed to be positive and sparsely represented in a dictionary of waveforms.
Piecing together the data fidelity and the prior terms, the solution to the
inverse problem is cast as the minimization of a non-smooth convex functional.
We establish the well-posedness of the optimization problem, characterize the
corresponding minimizers, and solve it by means of primal and primal-dual
proximal splitting algorithms originating from the field of non-smooth convex
optimization theory. Experimental results on deconvolution, inpainting and
denoising with some comparison to prior methods are also reported
Magnetic Interactions in BiFeO: a First-Principles Study
First-principles calculations, in combination with the four-state energy
mapping method, are performed to extract the magnetic interaction parameters of
multiferroic BiFeO. Such parameters include the symmetric exchange (SE)
couplings and the Dzyaloshinskii-Moriya (DM) interactions up to second nearest
neighbors, as well as the single ion anisotropy (SIA). All magnetic parameters
are obtained not only for the structural ground state, but also for the
and phases in order to determine the effects of
ferroelectricity and antiferrodistortion distortions, respectively, on these
magnetic parameters. In particular, two different second-nearest neighbor
couplings are identified and their origins are discussed in details. Moreover,
Monte-Carlo (MC) simulations using a magnetic Hamiltonian incorporating these
first-principles-derived interaction parameters are further performed. They
result (i) not only in the accurate prediction of the spin-canted G-type
antiferromagnetic structure and of the known magnetic cycloid propagating along
a direction, as well as their unusual characteristics (such
as a weak magnetization and spin-density-waves, respectively); (ii) but also in
the finding of another cycloidal state of low-energy and that awaits to be
experimentally confirmed. Turning on and off the different magnetic interaction
parameters in the MC simulations also reveal the precise role of each of them
on magnetism
A proximal iteration for deconvolving Poisson noisy images using sparse representations
We propose an image deconvolution algorithm when the data is contaminated by
Poisson noise. The image to restore is assumed to be sparsely represented in a
dictionary of waveforms such as the wavelet or curvelet transforms. Our key
contributions are: First, we handle the Poisson noise properly by using the
Anscombe variance stabilizing transform leading to a {\it non-linear}
degradation equation with additive Gaussian noise. Second, the deconvolution
problem is formulated as the minimization of a convex functional with a
data-fidelity term reflecting the noise properties, and a non-smooth
sparsity-promoting penalties over the image representation coefficients (e.g.
-norm). Third, a fast iterative backward-forward splitting algorithm is
proposed to solve the minimization problem. We derive existence and uniqueness
conditions of the solution, and establish convergence of the iterative
algorithm. Finally, a GCV-based model selection procedure is proposed to
objectively select the regularization parameter. Experimental results are
carried out to show the striking benefits gained from taking into account the
Poisson statistics of the noise. These results also suggest that using
sparse-domain regularization may be tractable in many deconvolution
applications with Poisson noise such as astronomy and microscopy
Data augmentation for galaxy density map reconstruction
The matter density is an important knowledge for today cosmology as many
phenomena are linked to matter fluctuations. However, this density is not
directly available, but estimated through lensing maps or galaxy surveys. In
this article, we focus on galaxy surveys which are incomplete and noisy
observations of the galaxy density. Incomplete, as part of the sky is
unobserved or unreliable. Noisy as they are count maps degraded by Poisson
noise. Using a data augmentation method, we propose a two-step method for
recovering the density map, one step for inferring missing data and one for
estimating of the density. The results show that the missing areas are
efficiently inferred and the statistical properties of the maps are very well
preserved
Dzyaloshinskii-Moryia interaction at an antiferromagnetic interface: first-principles study of FeIr bilayers on Rh(001)
We study the magnetic interactions in atomic layers of Fe and 5d
transition-metals such as Os, Ir, and Pt on the (001) surface of Rh using
first-principles calculations based on density functional theory. For both
stackings of the 5d-Fe bilayer on Rh(001) we observe a transition from an
antiferromagnetic to a ferromagnetic nearest-neighbor exchange interaction upon
5d band filling. In the sandwich structure 5d/Fe/Rh(001) the nearest neighbor
exchange is significantly reduced. For FeIr bilayers on Rh(001) we consider
spin spiral states in order to determine exchange constants beyond nearest
neighbors. By including spin-orbit coupling we obtain the Dzyaloshinskii-Moriya
interaction (DMI). The magnetic interactions in Fe/Ir/Rh(001) are similar to
those of Fe/Ir(001) for which an atomic scale spin lattice has been predicted.
However, small deviations between both systems remain due to the different
lattice constants and the Rh vs. Ir surface layers. This leads to slightly
different exchange constants and DMI and the easy magnetization direction
switches from out-of-plane for Fe/Ir(001) to in-plane for Fe/Ir/Rh(001).
Therefore a fine tuning of magnetic interactions is possible by using single 5d
transition-metal layers which may allow to tailor antiferromagnetic skyrmions
in this type of ultrathin films. In the sandwich structure Ir/Fe/Rh(001) we
find a strong exchange frustration due to strong hybridization of the Fe layer
with both Ir and Rh which drastically reduces the nearest-neighbor exchange.
The energy contribution from the DMI becomes extremely large and DMI beyond
nearest neighbors cannot be neglected. We attribute the large DMI to the low
coordination of the Ir layer at the surface. We demonstrate that higher- order
exchange interactions are significant in both systems which may be crucial for
the magnetic ground state
Inverse Problems with Poisson noise: Primal and Primal-Dual Splitting
In this paper, we propose two algorithms for solving linear inverse problems
when the observations are corrupted by Poisson noise. A proper data fidelity
term (log-likelihood) is introduced to reflect the Poisson statistics of the
noise. On the other hand, as a prior, the images to restore are assumed to be
positive and sparsely represented in a dictionary of waveforms. Piecing
together the data fidelity and the prior terms, the solution to the inverse
problem is cast as the minimization of a non-smooth convex functional. We
establish the well-posedness of the optimization problem, characterize the
corresponding minimizers, and solve it by means of primal and primal-dual
proximal splitting algorithms originating from the field of non-smooth convex
optimization theory. Experimental results on deconvolution and comparison to
prior methods are also reported
Deconvolution under Poisson noise using exact data fidelity and synthesis or analysis sparsity priors
In this paper, we propose a Bayesian MAP estimator for solving the
deconvolution problems when the observations are corrupted by Poisson noise.
Towards this goal, a proper data fidelity term (log-likelihood) is introduced
to reflect the Poisson statistics of the noise. On the other hand, as a prior,
the images to restore are assumed to be positive and sparsely represented in a
dictionary of waveforms such as wavelets or curvelets. Both analysis and
synthesis-type sparsity priors are considered. Piecing together the data
fidelity and the prior terms, the deconvolution problem boils down to the
minimization of non-smooth convex functionals (for each prior). We establish
the well-posedness of each optimization problem, characterize the corresponding
minimizers, and solve them by means of proximal splitting algorithms
originating from the realm of non-smooth convex optimization theory.
Experimental results are conducted to demonstrate the potential applicability
of the proposed algorithms to astronomical imaging datasets
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