Perpendicular Reading of Single Confined Magnetic Skyrmions

Thin-film sub-5 nm magnetic skyrmions constitute an ultimate scaling alternative for future digital data storage. Skyrmions are robust non-collinear spin-textures that can be moved and manipulated by small electrical currents. We show here an innovative technique to detect isolated nanoskyrmions with a current-perpendicular-to-plane geometry, which has immediate implications for device concepts. We explore the physics behind such a mechanism by studying the atomistic electronic structure of the magnetic quasiparticles. We investigate how the isolated skyrmion local-density-of-states which tunnels into the vacuum, when compared to the ferromagnetic background, is modified by the site-dependent spin-mixing of electronic states with different relative canting angles. Local transport properties are sensitive to this effect, as we report an atomistic conductance anisotropy of over 20% for magnetic skyrmions in Pd/Fe/Ir(111) thin-films. In single skyrmions, engineering this spin-mixing magnetoresistance possibly could be incorporated in future magnetic storage technologies

Pseudo spin-orbit coupling of Dirac particles in graphene spintronics

We study the pseudo spin-orbital (SO) effects experienced by massive Dirac particles in graphene, which can potentially be of a larger magnitude compared to the conventional Rashba SO effects experienced by particles in a 2DEG semiconductor heterostructure. In order to generate a uniform vertical pseudo SO field, we propose an artificial atomic structure, consisting of a graphene ring and a charged nanodot at the center which produces a large radial electric field. In this structure, a large pseudo SO coupling strength can be achieved by accelerating the Dirac particles around the ring, due to the small energy gap in graphene and the large radial electric field emanating from the charged nanodot. We discuss the theoretical possibility of harnessing the pseudo SO effects in mesoscopic applications, e.g. pseudo spin relaxation and switching.Comment: 12 pages, 1 figur

Graph Q-Learning for Combinatorial Optimization

Graph-structured data is ubiquitous throughout natural and social sciences, and Graph Neural Networks (GNNs) have recently been shown to be effective at solving prediction and inference problems on graph data. In this paper, we propose and demonstrate that GNNs can be applied to solve Combinatorial Optimization (CO) problems. CO concerns optimizing a function over a discrete solution space that is often intractably large. To learn to solve CO problems, we formulate the optimization process as a sequential decision making problem, where the return is related to how close the candidate solution is to optimality. We use a GNN to learn a policy to iteratively build increasingly promising candidate solutions. We present preliminary evidence that GNNs trained through Q-Learning can solve CO problems with performance approaching state-of-the-art heuristic-based solvers, using only a fraction of the parameters and training time

Group equivariant neural posterior estimation

Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientific models, however integrating them directly into expressive inference networks (such as normalizing flows) is not straightforward. We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data. Our method -- called group equivariant neural posterior estimation (GNPE) -- is based on self-consistently standardizing the "pose" of the data while estimating the posterior over parameters. It is architecture-independent, and applies both to exact and approximate equivariances. As a real-world application, we use GNPE for amortized inference of astrophysical binary black hole systems from gravitational-wave observations. We show that GNPE achieves state-of-the-art accuracy while reducing inference times by three orders of magnitude

Measurement of the charged pion mass using X-ray spectroscopy of exotic atoms

The $5g-4f$ transitions in pionic nitrogen and muonic oxygen were measured simultaneously by using a gaseous nitrogen-oxygen mixture at 1.4\,bar. Due to the precise knowledge of the muon mass the muonic line provides the energy calibration for the pionic transition. A value of (139.57077\,$\pm$\,0.00018)\,MeV/c$^{2}$ ($\pm$\,1.3ppm) is derived for the mass of the negatively charged pion, which is 4.2ppm larger than the present world average