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 BiFeO3_3: 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$_3$. 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 $R3c$ structural ground state, but also for the $R3m$ and $R\bar{3}c$ 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. $\ell_1$-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