Bands, resonances, edge singularities and excitons in core level spectroscopy investigated within the dynamical mean field theory

Using a recently developed impurity solver we exemplify how dynamical mean field theory captures band excitations, resonances, edge singularities and excitons in core level x-ray absorption (XAS) and core level photo electron spectroscopy (cPES) on metals, correlated metals and Mott insulators. Comparing XAS at different values of the core-valence interaction shows how the quasiparticle peak in the absence of core-valence interactions evolves into a resonance of similar shape, but different origin. Whereas XAS is rather insensitive to the metal insulator transition, cPES can be used, due to nonlocal screening, to measure the amount of local charge fluctuation

Measurement of solid precipitation with an optical disdrometer

A study about measurements of solid precipitation using an optical disdrometer is presented. The optical disdrometer is an improved version of the ODM 470 disdrometer. It allows to measure hydrometeors within a size range of 0.4 to 22 mm in diameter. <br><br> The main advantage of this instrument is its ability to estimate accurately precipitation even under strong wind conditions (Großklaus, 1996). To measure solid precipitation a geometrical model was developed to determine the mean cross-sectional area of snow crystals for different predefined shapes and sizes. It serves to develop an algorithm, which relates the mean cross sectional area of snow crystals to their maximum dimension, liquid water content, and terminal velocity. The algorithm was applied to disdrometer measurements during winter 1999/2000 in Uppsala/Sweden. Resulting precipitation was compared to independent measurements of a Geonor gauge and to manual measurements. In terms of daily precipitation the disdrometer shows a reliable performance

A Bayesian model for identifying hierarchically organised states in neural population activity

Neural population activity in cortical circuits is not solely driven by external inputs, but is also modulated by endogenous states. These cortical states vary on multiple time-scales and also across areas and layers of the neocortex. To understand information processing in cortical circuits, we need to understand the statistical structure of internal states and their interaction with sensory inputs. Here, we present a statistical model for extracting hierarchically organized neural population states from multi-channel recordings of neural spiking activity. We model population states using a hidden Markov decision tree with state-dependent tuning parameters and a generalized linear observation model. Using variational Bayesian inference, we estimate the posterior distribution over parameters from population recordings of neural spike trains. On simulated data, we show that we can identify the underlying sequence of population states over time and reconstruct the ground truth parameters. Using extracellular population recordings from visual cortex, we find that a model with two levels of population states outperforms a generalized linear model which does not include state-dependence, as well as models which only including a binary state. Finally, modelling of state-dependence via our model also improves the accuracy with which sensory stimuli can be decoded from the population response

High-density correlation energy expansion of the one-dimensional uniform electron gas

We show that the expression of the high-density (i.e small-$r_s$) correlation energy per electron for the one-dimensional uniform electron gas can be obtained by conventional perturbation theory and is of the form \Ec(r_s) = -\pi^2/360 + 0.00845 r_s + ..., where $r_s$ is the average radius of an electron. Combining these new results with the low-density correlation energy expansion, we propose a local-density approximation correlation functional, which deviates by a maximum of 0.1 millihartree compared to the benchmark DMC calculations.Comment: 7 pages, 2 figures, 3 tables, accepted for publication in J. Chem. Phy

Quantitative determination of bond order and lattice distortions in nickel oxide heterostructures by resonant x-ray scattering

We present a combined study of Ni $K$-edge resonant x-ray scattering and density functional calculations to probe and distinguish electronically driven ordering and lattice distortions in nickelate heterostructures. We demonstrate that due to the low crystal symmetry, contributions from structural distortions can contribute significantly to the energy-dependent Bragg peak intensities of a bond-ordered NdNiO$_3$ reference film. For a LaNiO$_3$-LaAlO$_3$ superlattice that exhibits magnetic order, we establish a rigorous upper bound on the bond-order parameter. We thus conclusively confirm predictions of a dominant spin density wave order parameter in metallic nickelates with a quasi-two-dimensional electronic structure

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

The role of ice particle shapes and size distributions in the single scattering properties of cirrus clouds

The roles of ice particle size distributions (SDs) and particle shapes in cirrus cloud solar radiative transfer are investigated by analyzing SDs obtained from optical array probe measurements (particle sizes larger than 20–40 μm) during intensive field observations of the International Cirrus Experiment, the European Cloud and Radiation Experiment, the First ISCCP Regional Experiment, and the Central Equatorial Pacific Experiment. It is found that the cloud volume extinction coefficient is more strongly correlated with the total number density than with the effective particle size. Distribution-averaged mean single scattering properties are calculated for hexagonal columns, hexagonal plates, and polycrystals at a nonabsorbing (0.5 μm), moderately absorbing (1.6 μm), and strongly absorbing (3.0 μm) wavelength. At 0.5 μm (1.6 μm) (3.0 μm), the spread in the resulting mean asymmetry parameters due to different SDs is smaller than (comparable to) (smaller than) the difference caused by applying different particle shapes to these distributions. From a broadband solar radiative transfer point of view it appears more important to use the correct particle shapes than to average over the correct size distributions

Heliostat Testing according to SolarPACES Task III Guideline

The SolarPACES Guideline for Heliostat Performace Testing finally provides a solid base for standardized testing and comparison as well as the definitions of essential heliostat parameters such as slope and tracking errors. SBPS is running an extensive test program for their 4 Stellio preseries heliostats at the DLR Solar Tower in Juelich, Germany until summer 2019. Additional objective is to accumulate operating hours and evaluate long-term effects on the Stellio performance quality. Slope error measurement has been performed by CSPS and is repeated every 3 months. First results show 1D slope errors of 0.7 to 1.2 mrad. Tracking performance could not have been concluded due to missing final measurements of the kinematic system of each heliostat which is necessary for calibration. However, beam centroid evaluation software has been tested with first uncalibrated tracking hours and is prepared for normal operation. First photogrammetric measurements have been performed to characterize the dead weight deflection of the heliostat in 15 different azimuth and elevation combinations. This has been prepared and implemented in Rhino CAD. Adaptions may be necessary to include pylon deflection as well

Electronic depth profiles with atomic layer resolution from resonant soft x-ray reflectivity

The analysis of x-ray reflectivity data from artificial heterostructures usually relies on the homogeneity of optical properties of the constituent materials. However, when the x-ray energy is tuned to an absorption edge, this homogeneity no longer exists. Within the same material, spatial regions containing elements at resonance will have optical properties very different from regions without resonating sites. In this situation, models assuming homogeneous optical properties throughout the material can fail to describe the reflectivity adequately. As we show here, resonant soft x-ray reflectivity is sensitive to these variations, even though the wavelength is typically large as compared to the atomic distances over which the optical properties vary. We have therefore developed a scheme for analyzing resonant soft x-ray reflectivity data, which takes the atomic structure of a material into account by "slicing" it into atomic planes with characteristic optical properties. Using LaSrMnO4 as an example, we discuss both the theoretical and experimental implications of this approach. Our analysis not only allows to determine important structural information such as interface terminations and stacking of atomic layers, but also enables to extract depth-resolved spectroscopic information with atomic resolution, thus enhancing the capability of the technique to study emergent phenomena at surfaces and interfaces.Comment: Completely overhauled with respect to the previous version due to peer revie

Strain and composition dependence of the orbital polarization in nickelate superlattices

A combined analysis of x-ray absorption and resonant reflectivity data was used to obtain the orbital polarization profiles of superlattices composed of four-unit-cell-thick layers of metallic LaNiO3 and layers of insulating RXO3 (R=La, Gd, Dy and X=Al, Ga, Sc), grown on substrates that impose either compressive or tensile strain. This superlattice geometry allowed us to partly separate the influence of epitaxial strain from interfacial effects controlled by the chemical composition of the insulating blocking layers. Our quantitative analysis reveal orbital polarizations up to 25%. We further show that strain is the most effective control parameter, whereas the influence of the chemical composition of the blocking layers is comparatively small.Comment: 9 pages, 8 figure