Bimeron nanoconfined design

We report on the stabilization of the topological bimeron excitations in confined geometries. The Monte Carlo simulations for a ferromagnet with a strong Dzyaloshinskii-Moriya interaction revealed the formation of a mixed skyrmion-bimeron phase. The vacancy grid created in the spin lattice drastically changes the picture of the topological excitations and allows one to choose between the formation of a pure bimeron and skyrmion lattice. We found that the rhombic plaquette provides a natural environment for stabilization of the bimeron excitations. Such a rhombic geometry can protect the topological state even in the absence of the magnetic field.Comment: 5 pages, 7 figure

Profile approach for recognition of three-dimensional magnetic structures

We propose an approach for low-dimensional visualisation and classification of complex topological magnetic structures formed in magnetic materials. Within the approach one converts a three-dimensional magnetic configuration to a vector containing the only components of the spins that are parallel to the z axis. The next crucial step is to sort the vector elements in ascending or descending order. Having visualized profiles of the sorted spin vectors one can distinguish configurations belonging to different phases even with the same total magnetization. For instance, spin spiral and paramagnetic states with zero total magnetic moment can be easily identified. Being combined with a simplest neural network our profile approach provides a very accurate phase classification for three-dimensional magnets characterized by complex multispiral states even in the critical areas close to phases transitions. By the example of the skyrmionic configurations we show that profile approach can be used to separate the states belonging to the same phase

Supervised learning magnetic skyrmion phases

We propose and apply simple machine learning approaches for recognition and classification of complex non-collinear magnetic structures in two-dimensional materials. The first approach is based on the implementation of the single-hidden-layer neural network that only relies on the z projections of the spins. In this setup one needs a limited set of magnetic configurations to distinguish ferromag- netic, skyrmion and spin spiral phases, as well as their different combinations in transitional areas of the phase diagram. The network trained on the configurations for square-lattice Heisenberg model with Dzyaloshinskii-Moriya interaction can classify the magnetic structures obtained from Monte Carlo calculations for triangular lattice and vice versa. The second approach we apply, a minimum distance method performs a fast and cheap classification in cases when a particular configuration is to be assigned to only one magnetic phase. The methods we propose are also easy to use for analysis of the numerous experimental data collected with spin-polarized scanning tunneling microscopy and Lorentz transmission electron microscopy experiments.Comment: 9 pages, 14 figures. Accepted for publication in Physical Review

Quantifying spatio-temporal patterns in classical and quantum systems out of equilibrium

A rich variety of non-equilibrium dynamical phenomena and processes unambiguously calls for the development of general numerical techniques to probe and estimate a complex interplay between spatial and temporal degrees of freedom in many-body systems of completely different nature. In this work we provide a solution to this problem by adopting a structural complexity measure to quantify spatio-temporal patterns in the time-dependent digital representation of a system. On the basis of very limited amount of data our approach allows to distinguish different dynamical regimes and define critical parameters in both classical and quantum systems. By the example of the discrete time crystal realized in non-equilibrium quantum systems we provide a complete low-level characterization of this nontrivial dynamical phase with only processing bitstrings, which can be considered as a valuable alternative to previous studies based on the calculations of qubit correlation functions.Comment: Monitoring a quantum evolution has been adde

Estimating Patterns of Classical and Quantum Skyrmion States

In this review we discuss the latest results concerning development of the machine learning algorithms for characterization of the magnetic skyrmions that are topologically-protected magnetic textures originated from the Dzyaloshinskii-Moriya interaction that competes Heisenberg isotropic exchange in ferromagnets. We show that for classical spin systems there is a whole pool of machine approaches allowing their accurate phase classification and quantitative description on the basis of few magnetization snapshots. In turn, investigation of the quantum skyrmions is a less explored issue, since there are fundamental limitations on the simulation of such wave functions with classical supercomputers. One needs to find the ways to imitate quantum skyrmions on near-term quantum computers. In this respect, we discuss implementation of the method for estimating structural complexity of classical objects for characterization of the quantum skyrmion state on the basis of limited number of bitstrings obtained from the projective measurements

Glass-based charged particle detector performance for Horizon-T EAS detector system

An implementation of a novel of glass-based detector with fast response and wide detection range is needed to increase resolution for ultra-high energy cosmic rays detection. Such detector has been designed and built for the Horizon-T detector system at Tien Shan high-altitude Science Station. The main characteristics, such as design, duration of the detector pulse and calibration of a single particle response are discussed.Comment: Simulation is used to assess glass detector performance. Simulation is validated first when compared to scintillator detector experimental measurements. Final results summarized in table. Updated May 2017 with calibrations updat

An effective spin model on the honeycomb lattice for the description of magnetic properties in two-dimensional Fe3_3GeTe2_2

Fe$_3$GeTe$_2$ attracts significant attention due to technological perspectives of realizing room temperature ferromagnetism in two-dimensional materials. Here we show that due to structural peculiarities of the Fe$_3$GeTe$_2$ monolayer, short distance between the neighboring iron atoms induces a strong exchange coupling. This strong coupling allows us to consider them as an effective cluster with a magnetic moment $\sim$5 $\mu_B$, giving rise to a simplified spin model on a bipartite honeycomb lattice with the reduced number of long-range interactions. The simplified model perfectly reproduces the results of the conventional spin model, but allows for a more tractable description of the magnetic properties of Fe$_3$GeTe$_2$, which is important, e.g., for large-scale simulations. Also, we discuss the role of biaxial strain in the stabilization of ferromagnetic ordering in Fe$_3$GeTe$_2$.Comment: 7 pages, 7 figure