Photoemission Spectroscopic Evidence of Multiple Dirac Cones in Superconducting BaSn$_3$

The signatures of topological superconductivity (TSC) in the superconducting materials with topological nontrivial states prompt intensive researches recently. Utilizing high-resolution angle-resolved photoemission spectroscopy and first-principles calculations, we demonstrate multiple Dirac fermions and surface states in superconductor BaSn$_3$ with a critical transition temperature of about 4.4 K. We predict and then unveil the existence of two pairs of type-uppercaseexpandafter{romannumeral1} topological Dirac fermions residing on the rotational axis. Type-uppercaseexpandafter{romannumeral2} Dirac fermions protected by screw axis are confirmed in the same compound. Further calculation for the spin helical texture of the observed surface states originating from the Dirac fermions give an opportunity for realization of TSC in one single material. Hosting multiple Dirac fermions and topological surface states, the intrinsic superconductor BaSn$_3$ is expected to be a new platform for further investigation of the topological quantum materials as well as TSC.

Single crystal growth and physical properties of pyroxene CoGeO$_3$

We report on the synthesis and physical properties of cm-sized CoGeO$_3$ single crystals grown in a high pressure mirror furnace at pressures of 80~bar. Direction dependent magnetic susceptibility measurements on our single crystals reveal highly anisotropic magnetic properties that we attribute to the impact of strong single ion anisotropy appearing in this system with T$_N$~$sim$~33.5~K. Furthermore, we observe effective magnetic moments that are exceeding the spin only values of the Co ions which reveals the presence of sizable orbital moments in CoGeO$_3$.

Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution

The unscented Kalman inversion (UKI) method presented in [1] is a general derivative-free approach for the inverse problem. UKI is particularly suitable for inverse problems where the forward model is given as a black box and may not be differentiable. The regularization strategies, convergence property, and speed-up strategies [1,2] of the UKI are thoroughly studied, and the method is capable of handling noisy observation data and solving chaotic inverse problems. In this paper, we study the uncertainty quantification capability of the UKI. We propose a modified UKI, which allows to well approximate the mean and covariance of the posterior distribution for well-posed inverse problems with large observation data. Theoretical guarantees for both linear and nonlinear inverse problems are presented. Numerical results, including learning of permeability parameters in subsurface flow and of the Navier-Stokes initial condition from solution data at positive times are presented. The results obtained by the UKI require only $O(10)$ iterations, and match well with the expected results obtained by the Markov Chain Monte Carlo method.

Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model

The unscented Kalman inversion (UKI) presented in [1] is a general derivative-free approach to solving the inverse problem. UKI is particularly suitable for inverse problems where the forward model is given as a black box and may not be differentiable. The regularization strategy and convergence property of the UKI are thoroughly studied, and the method is demonstrated effectively handling noisy observation data and solving chaotic inverse problems. In this paper, we aim to make the UKI more efficient in terms of computational and memory costs for large scale inverse problems. We take advantages of the low-rank covariance structure to reduce the number of forward problem evaluations and the memory cost, related to the need to propagate large covariance matrices. And we leverage reduced-order model techniques to further speed up these forward evaluations. The effectiveness of the enhanced UKI is demonstrated on a barotropic model inverse problem with O($10^5$) unknown parameters and a 3D generalized circulation model (GCM) inverse problem, where each iteration is as efficient as that of gradient-based optimization methods.

Unscented Kalman Inversion

A useful approach to solve inverse problems is to pair the parameter-to-data map with a stochastic dynamical system for the parameter, and then employ techniques from filtering to estimate the parameter given the data. Three classical approaches to filtering of nonlinear systems are the extended, ensemble and unscented Kalman filters. The extended Kalman inversion (ExKI) is impractical when the forward map is not readily differentiable and given as a black box, and also for high dimensional parameter spaces because of the need to propagate large covariance matrices. Ensemble Kalman inversion (EKI) has emerged as a useful tool which overcomes both of these issues: it is derivative free and works with a low-rank covariance approximation formed from the ensemble. In this paper, we demonstrate that unscented Kalman methods also provide an effective tool for derivative-free inversion in the setting of black-box forward models, introducing unscented Kalman inversion (UKI). Theoretical analysis is provided for linear inverse problems, and a smoothing property of the data mis-fit under the unscented transform is explained. We provide numerical experiments, including various applications: learning subsurface flow permeability parameters; learning the structure damage field; learning the Navier-Stokes initial condition; and learning subgrid-scale parameters in a general circulation model. The theory and experiments show that the UKI outperforms the EKI on parameter learning problems with moderate numbers of parameters and outperforms the ExKI on problems where the forward model is not readily differentiable, or where the derivative is very sensitive. In particular, UKI based methods are of particular value for parameter estimation problems in which the number of parameters is moderate but the forward model is expensive and provided as a black box which is impractical to differentiate.

Nonlocal effects in negative triangularity TCV plasmas

Global gradient driven GENE gyrokinetic simulations are used to investigate TCV plasmas with negative triangularity. Considering a limited L-mode plasma, corresponding to an experimental triangularity scan, numerical results are able to reproduce the actual transport level over a major fraction of the plasma minor radius for a plasma with $delta_{rm LCFS}=-0.3$ and its equivalent with standard positive triangularity $delta$. For the same heat flux, a larger electron temperature gradient is sustained by $delta<0$, in turn resulting in an improved electron energy confinement. Consistently with the experiments, a reduction of the electron density fluctuations is also seen. Local flux-tube simulations are used to gauge the magnitude of nonlocal effects. Surprisingly, very little differences are found between local and global approaches for $delta>0$, while local results yield a strong overestimation of the heat fluxes when $delta<0$. Despite the high sensitivity of the turbulence level with respect to the input parameters, global effects appear to play a crucial role in the negative triangularity plasma and must be retained to reconcile simulations and experiments. Finally, a general stabilizing effect of negative triangularity, reducing fluxes and fluctuations by a factor dependent on the actual profiles, is recovered.

Accelerator and Beam Physics Research Goals and Opportunities

This report is a summary of two preparatory workshops, documenting the community vision for the national accelerator and beam physics research program. It identifies the Grand Challenges of accelerator and beam physics (ABP) field and documents research opportunities to address these Grand Challenges. This report will be used to develop a strategic research roadmap for the field of accelerator science.

Power Iteration for Tensor PCA

In this paper, we study the power iteration algorithm for the spiked tensor model, as introduced in [44]. We give necessary and sufficient conditions for the convergence of the power iteration algorithm. When the power iteration algorithm converges, for the rank one spiked tensor model, we show the estimators for the spike strength and linear functionals of the signal are asymptotically Gaussian; for the multi-rank spiked tensor model, we show the estimators are asymptotically mixtures of Gaussian. This new phenomenon is different from the spiked matrix model. Using these asymptotic results of our estimators, we construct valid and efficient confidence intervals for spike strengths and linear functionals of the signals.

Dual topological superconducting states in the layered titanium-based oxypnictide superconductor BaTi$_2$Sb$_2$O

Topological superconductors have long been predicted to host Majorana zero modes which obey non-Abelian statistics and have potential for realizing non-decoherence topological quantum computation. However, material realization of topological superconductors is still a challenge in condensed matter physics. Utilizing high-resolution angle-resolved photoemission spectroscopy and first-principles calculations, we predict and then unveil the coexistence of topological Dirac semimetal and topological insulator states in the vicinity of Fermi energy ($E_F$) in the titanium-based oxypnictide superconductor BaTi$_2$Sb$_2$O. Further spin-resolved measurements confirm its spin-helical surface states around $E_F$, which are topologically protected and give an opportunity for realization of Majorana zero modes and Majorana flat bands in one material. Hosting dual topological superconducting states, the intrinsic superconductor BaTi$_2$Sb$_2$O is expected to be a promising platform for further investigation of topological superconductivity.

A Computationally Tractable Framework for Nonlinear Dynamic Multiscale Modeling of Membrane Fabric

A general-purpose computational homogenization framework is proposed for the nonlinear dynamic analysis of membranes exhibiting complex microscale and/or mesoscale heterogeneity characterized by in-plane periodicity that cannot be effectively treated by a conventional method, such as woven fabrics. The framework is a generalization of the finite element squared (or FE2) method in which a localized portion of the periodic subscale structure is modeled using finite elements. The numerical solution of displacement driven problems involving this model can be adapted to the context of membranes by a variant of the Klinkel-Govindjee method[1] originally proposed for using finite strain, three-dimensional material models in beam and shell elements. This approach relies on numerical enforcement of the plane stress constraint and is enabled by the principle of frame invariance. Computational tractability is achieved by introducing a regression-based surrogate model informed by a physics-inspired training regimen in which FE$^2$ is utilized to simulate a variety of numerical experiments including uniaxial, biaxial and shear straining of a material coupon. Several alternative surrogate models are evaluated including an artificial neural network. The framework is demonstrated and validated for a realistic Mars landing application involving supersonic inflation of a parachute canopy made of woven fabric.