Log-Gaussian processes for AI-assisted TAS experiments

To understand the origins of materials properties, neutron scattering experiments at three-axes spectrometers (TAS) investigate magnetic and lattice excitations in a sample by measuring intensity distributions in its momentum (Q) and energy (E) space. The high demand and limited availability of beam time for TAS experiments however raise the natural question whether we can improve their efficiency or make better use of the experimenter's time. In fact, using TAS, there are a number of scientific questions that require searching for signals of interest in a particular region of Q-E space, but when done manually, it is time consuming and inefficient since the measurement points may be placed in uninformative regions such as the background. Active learning is a promising general machine learning approach that allows to iteratively detect informative regions of signal autonomously, i.e., without human interference, thus avoiding unnecessary measurements and speeding up the experiment. In addition, the autonomous mode allows experimenters to focus on other relevant tasks in the meantime. The approach that we describe in this article exploits log-Gaussian processes which, due to the log transformation, have the largest approximation uncertainties in regions of signal. Maximizing uncertainty as an acquisition function hence directly yields locations for informative measurements. We demonstrate the benefits of our approach on outcomes of a real neutron experiment at the thermal TAS EIGER (PSI) as well as on results of a benchmark in a synthetic setting including numerous different excitations.Comment: Main: 22 pages, 5 figures | Extended Data: 8 figures | Supplementary Information: 5 pages, 2 figure

Amortized Bayesian Inference of GISAXS Data with Normalizing Flows

Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging technique used in material research to study nanoscale materials. Reconstruction of the parameters of an imaged object imposes an ill-posed inverse problem that is further complicated when only an in-plane GISAXS signal is available. Traditionally used inference algorithms such as Approximate Bayesian Computation (ABC) rely on computationally expensive scattering simulation software, rendering analysis highly time-consuming. We propose a simulation-based framework that combines variational auto-encoders and normalizing flows to estimate the posterior distribution of object parameters given its GISAXS data. We apply the inference pipeline to experimental data and demonstrate that our method reduces the inference cost by orders of magnitude while producing consistent results with ABC

Formation of metal nano-size clusters with a DC magnetron-based gas aggregation source

Nano-size silver and copper clusters were produced with a DC magnetron-based gas aggregation source. The typical mass of the studied clusters was in the range of 10000 atoms for copper clusters, and in the range of 1000 atoms for silver clusters. The processes of cluster formation, cluster charging and cluster flow were investigated. Technique for measurement of cluster ion velocity distribution functions was developed and applied. Influence of the magnetron target erosion on the mass spectra was systematically investigated and quantitatively characterized. Results of the present work include an experimental and theoretical investigation of the effects, which are of great importance for the production of cluster beams with the desired properties.Nanometer-große Silber und Kupfer-Cluster wurden mit einer auf dem DC-Magnetron-Prinzip-basierenden Gasaggregationsquelle erzeugt. Die typische Masse der untersuchten Cluster liegt im Bereich von 10000 Atomen für Kupfercluster und im Bereich von 1000 Atomen für Silbercluster. Die Prozesse der Cluster-Bildung und Cluster-Aufladung und das Ausströmen des Cluster wurden untersucht. Die Technik für die Messung der Clustergeschwindigkeit-Verteilungsfunktion wurde entwickelt und angewandt. Der Einfluss der Magnetron-Targeterosion auf die Massenspektren wurde systematisch untersucht und quantitativ charakterisiert. Die Ergebnisse der vorliegenden Arbeit beinhalten die experimentelle und theoretische Untersuchung der Auswirkungen, die für die Herstellung von Cluster-Strahlen mit den gewünschten Eigenschaften von großer Bedeutung sind

Deep learning for X-ray or neutron scattering under grazing-incidence: extraction of distributions

Grazing-incidence small-angle scattering (GISAS) is a technique of significant importance for the investigation of thin multilayered films containing nano-sized objects. It provides morphology information averaged over the sample area. However, this averaging together with multiple reflections and the well-known phase problem make the data analysis challenging and time consuming. In the present paper we show that densely connected neural networks (DenseNets) can be applied for GISAS data analysis and deliver fast and plausible results. The extraction of the rotational distributions of hexagonal nanoparticle arrangements is taken as a case study

Directional sensitivity of MuSTAnG muon telescope

We investigate directional sensitivity of MuSTAnG muon telescope by deriving the distribution of secondary muons, which create the counting rate of telescope, by asymptotic directions of primary protons. This distribution, defined as “directivity function”, allows us to clarify protons appearing from which direction essentially contribute to counting rate of detector. Directivity function has different behavior for the muons falling on the telescope at different zenith and polar angles. Vertical, West, and East fluxes exhibit strong maximums near the asymptotic longitude about 61°, whereas North and South fluxes have larger spread distributions. About 65% of muons, which create the Vertical counting rate of MuSTAnG, are produced by the primary protons, coming in the interval of asymptotic longitudes about (50°, 80°). Using directivity function will allow one to more correctly determine the location of interplanetary disturbances. Analogous analysis, made for other muon detectors, will clarify their directional sensitivities, improving by this the forecasting capability of network of ground-based muon detectors

Atmospheric effect corrections of MuSTAnG data

The atmospheric effect correction of the muon flux measured by ground level telescopes is of special importance for further study of cosmic ray variations. The Duperier method is used to correct atmospheric effects on the muon intensity observed by the MuSTAnG telescope. Linear multiple correlation and regression analysis are applied to the data registered during the year 2009. The aerological data are obtained from daily radiosonde balloon flights of Deutscher Wetterdienst. The regression coefficients and total correlation coefficients are calculated for all directional channels. The seasonal variations are eliminated from the MuSTAnG telescope data. The results are compared with theoretical elimination of temperature variations

Atmospheric effect corrections of MuSTAnG data

The atmospheric effect correction of the muon flux measured by ground level telescopes is of special importance for further study of cosmic ray variations. The Duperier method is used to correct atmospheric effects on the muon intensity observed by the MuSTAnG telescope. Linear multiple correlation and regression analysis are applied to the data registered during the year 2009. The aerological data are obtained from daily radiosonde balloon flights of Deutscher Wetterdienst. The regression coefficients and total correlation coefficients are calculated for all directional channels. The seasonal variations are eliminated from the MuSTAnG telescope data. The results are compared with theoretical elimination of temperature variations

Angular and velocity distribution of nano-size cluster beams in a gas flow

The present work analyzes the cluster relaxation in the gas beam for clusters formed in a gas aggregation nanocluster sources in the framework of the small transition region model. The model applied provides plausible values for cluster velocities which are in a good agreement with measured ones and in the fair agreement with simulated by other researchers. The effect of separation of clusters of different masses in the beam due to dependence of their transversal velocity on the cluster mass is also studied

Developing BornAgain graphical user interface: lessons learned

Designing a user interface is only one of many aspects of the development of an entire application.However, a good user interface encourages an easy and natural interaction between a user and a systemand, at the end of the day, is the most vital key to user productivity and happiness.Designing a good user interface is a challenging and time consuming task.It can be roughly splitted on two lousely related parts: design of the visual composition of an application and design of internal application structure.In this discussion we are going to focus on aspects of internal design of large GUI applications, leaving questions of usability and user experience aside.We will give a brief overview of the most common GUI design patternsto separate internal representation of the information from the ways the information is presented to and accepted from the user. We will summarize our experiences acquired through the developmentof BornAgain graphical user interface, explain its internal structure and willtry to formulate some practical advices for large GUI's design