Using the Asymmetric Gaussian Method to Interpret the Electronic Spectroscopy of the Olivine Family

This paper discusses the mathematical aspects of band fitting and introduces the Asymmetric Gaussian curve and its tangent space for the first time. First, we derive an equation for an Asymmetric Gaussian shape. We then derive a rule for the resolution of two Gaussian shaped bands. We then use the Asymmetrical Gaussian equation to derive a Master Equation to fit two overlapping bands. We identify regions of the fitting space where the Asymmetric Gaussian fit is optimal, sub optimal and not optimal. We then demonstrate the use of the Asymmetric Gaussian curve to fit four overlapping Gaussian bands, and show how this is relevant to the olivine family spectral complex at 1 μm. We develop a modified model of the olivine family spectral complex based on previous work by Runciman and Burns. The limitations of the asymmetric band fitting method and a critical assessment of three commonly used numerical minimization methods are also provided.

Fiber statistics of nonwoven materials by SEM images - influence of number of images

Electrospun nonwovens are widely applied in biomedicine and various other fields. For control of the manufacturing process and quality assurance Scanning electron microscopy (SEM) imaging is one standard practice. In this study, statistical datasets of 60 SEM images of three nonwoven samples were evaluated using Gaussian fit to obtain numerical results of their fiber diameter distributions. The question of how much effort is required for acceptable imaging and processing is being discussed. As determined here, for reliable statistics, a minimum surface area of the nonwoven has to be evaluated. The fiber diameter should be in a range of approximately 2 - 3% of the edge length of the square equivalent of the evaluated image area, using sufficiently magnified SEM images, in which the fiber diameter is imaged over at least 30 pixels.

Development of an image processing method for wake meandering studies and its application on data sets from scanning wind lidar and large-eddy simulation

Wake meandering studies require knowledge of the instantaneous wake shape and its evolution. Scanning lidar data are used to identify the wake pattern behind offshore wind turbines but do not immediately reveal the wake shape. The precise detection of the wake shape and centerline helps to build models predicting wake behavior. The conventional Gaussian fit methods are reliable in the near-wake area but lose precision with the distance from the rotor and require good data resolution for an accurate fit. The thresholding methods usually imply a fixed value or manual selection of a threshold, which hinders the wake detection on a large data set. We propose an automatic thresholding method for the wake shape and centerline detection, which is less dependent on the data resolution and can also be applied to the image data. We show that the method performs reasonably well on large-eddy simulation data and apply it to the data set containing lidar measurements of the two wakes. Along with the wake detection method, we use image processing statistics, such as entropy analysis, to filter and classify lidar scans. The image processing method is developed to reduce dependency on the supplementary reference data such as wind speed and direction. We show that the centerline found with the image processing is in a good agreement with the manually detected centerline and the Gaussian fit method. We also discuss a potential application of the method to separate the near and far wakes and to estimate the wake direction.

Examining the Effects of Fluid Velocity Gradients on 4D Digital Holographic PIV/PTV Measurements

4D digital holographic PIV/PTV (4D-DHPIV/PTV) methods have demonstrated theoretical viability due to their relative ease of setup and high spatial resolution (Soria (2018)). This study investigates how velocity gradients related to different flow regimes and their magnitudes affect 3-component–3-dimensional (3C-3D) digital holographic PIV measurement uncertainty. The error introduced by velocity gradients within the interrogation volume is studied by simulating particles in a velocity field, with a given constant velocity gradient superimposed on a uniform flow from which a time-series of hologram pairs are generated and the 3C-3D velocity fields and their errors are determined using 4D-DHPIV/PTV Sun et al. (2020). Hologram pairs are simulated by modelling the propagation and particlediffraction of coherent laser light using the angular spectrum method (Goodman (1996)). The hologram reconstruction then involves direct reconstruction, followed by deconvolution, a particle position refinement and a hologram subtraction step (Sun et al. (2020)). The particle positions obtained from 4D-DHPIV/PTV are then used to resolve particle displacement measurements using 3D cross-correlation digital analysis with a 3D Gaussian fit to sub-pixel resolution (Soria (2006)). The effects of velocity gradients on the displacement uncertainty and bias error have been investigated by undertaking Monte Carlo simulations under a range of velocity gradient environments. Specifically, 5 common velocity gradients have been studied, which included pure strain, pure vorticity and x, y and z-directional shear. The results indicate that the novel 4D-DHPIV/PTV has poorer accuracy and precision in the z-propagation axis, resulting in larger minimum uncertainties and bias errors. The errors in the z axis are also significantly less affected by velocity gradients in the z direction when compared to the effects of x and y directional velocity gradients on x and y errors respectively. Furthermore, the rate of cross-correlation maximum and SNR decrease are approximately 1.36 times slower due to velocity gradients in the z axis than other axes.

Brittle Deformation in the Neoproterozoic Basement of Southeast Brazil: Traces of Intraplate Cenozoic Tectonics

The basement of southeast Brazil is traditionally interpreted as the result of Neoproterozoic and early Paleozoic orogenic cycles. Wide regions of the Atlantic Plateau (southeast Brazil) are characterized by rocks and tectonic structures of Precambrian age. According to the classical literature, these regions have not been affected by tectonics since the Miocene, despite the fact that they rest close to Cenozoic basins, which have suffered recent tectonic deformation. The objective of this research is to evaluate the role of neotectonics in the Atlantic Plateau. This task is accomplished through a multiscalar approach which includes lineament domain analysis from regionally sized digital elevation models and structural geology field surveys. Lineaments are automatically detected and statistically analyzed. Azimuthal analyses of data on faults and fractures by a polynomial Gaussian fit enables the identification of the main structural trends. Fault-slip direct inversion by means of the original Monte Carlo approach allows one to compute the multiple paleostresses that produced the measured fault population. The results show the presence of a principal ENE–WSW lineament domain, related to an old shear zone possibly reactivated since the Miocene. One of the paleostresses computed from fault-slip inversion is in agreement with the neotectonic stress-field proposed by other authors.

Influence of indentation size and spacing on statistical phase analysis via high-speed nanoindentation mapping of metal alloys

The development of high-speed nanoindentation has enabled the acquisition of mechanical property maps over square millimeters of area with micron-scale resolution in reasonable amounts of time. This provides rich datasets which contain morphological and statistical data on the variation of mechanical properties in a microstructure. However, the influences of the indentation size and the deconvolution method employed on the extracted phase properties remain unclear. In this work, a range of depth/spacing increments was explored on two different materials systems, an Al-Cu eutectic alloy and a duplex stainless steel, representing an ‘easy’ and a ‘hard’ case for statistical deconvolution, respectively. A total of ~ 500,000 indentations were performed. A variety of statistical analyses were then employed and compared: the 1D analysis of Ulm et al. using 2 and 3 phases, a 2D rotated Gaussian fit, K-means clustering, and a visual comparison to 2D histograms. This revealed several different sensitivities of the deconvolution methods to various types of error in phase identification. Graphic abstract

Long-term stellar activity variations and their effect on radial-velocity measurements

Long-term stellar activity variations can affect the detectability of long-period and Earth-analogue extrasolar planets. We have, for 54 stars, analysed the long-term trend of five activity indicators: log $R^{\prime }_\mathrm{{HK}}$, the cross-correlation function (CCF) bisector span, CCF full-width-at-half-maximum, CCF contrast, and the area of the Gaussian fit to the CCF; and studied their correlation with the RVs. The sign of the correlations appears to vary as a function of stellar spectral type, and the transition in sign signals a noteworthy change in the stellar activity properties where earlier type stars appear more plage dominated. These transitions become more clearly defined when considered as a function of the convective zone depth. Therefore, it is the convective zone depth (which can be altered by stellar metallicity) that appears to be the underlying fundamental parameter driving the observed activity correlations. In addition, for most of the stars, we find that the RVs become increasingly red-shifted as activity levels increase, which can be explained by the increase in the suppression of convective blue-shift. However, we also find a minority of stars where the RVs become increasingly blue-shifted as activity levels increase. Finally, using the correlation found between activity indicators and RVs, we removed RV signals generated by long-term changes in stellar activity. We find that performing simple cleaning of such long-term signals enables improved planet detection at longer orbital periods.

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

A comparative study of the strength of flickering in cataclysmic variables

ABSTRACT Flickering is a universal phenomenon in accreting astronomical systems that still defies detailed physical understanding. It is particularly evident in cataclysmic variables (CVs). Attempting to define boundary conditions for models, the strength of the flickering is measured in several thousand light curves of more than 100 CVs. The flickering amplitude is parametrized by the full width at half-maximum of a Gaussian fit to the magnitude distribution of data points in a light curve. This quantity requires several corrections before a comparison between different sources can be made. While no correlations of the flickering strength with simple parameters such as component masses, orbital inclination, or period were detected, a dependence on the absolute magnitude of the primary component and on the CV subtype is found. In particular, flickering in VY Scl type novalike variables is systematically stronger than in UX UMa type novalikes. The broad-band spectrum of the flickering light source can be fit by simple models but shows excess in the U band. When the data permitted to investigate the flickering strength as a function of orbital phase in eclipsing CVs, such a dependence was found, but it is different for different systems. Surprisingly, there are also indications for variations of the flickering strength with the superhump phase in novalike variables with permanent superhumps. In dwarf novae, the flickering amplitude is high during quiescence, drops quickly at an intermediate magnitude when the system enters into (or returns from) an outburst and, on average, remains constant above a given brightness threshold.

Study of star formation rate and metallicity of the low redshift (z < 0.02) dwarf galaxies

In this paper, we have presented an analysis of emission lines from two dwarf galaxies. We analyzed the strongest emission lines of wavelength ranging from 4100 Å to 6700 Å. Among these emission lines, Hα and OIII have the highest intensities with 113.09×10-17 erg/s/cm2/Å and 142.12×10-17 erg/s/cm2/Å in the galaxies SDSSJ222726.64+120539.8 and SDSSJ162753.47+482529.3, respectively. The Gaussian fit carried out in these emission lines showed the perfect fits with regression coefficient greater than 98 %, and full width half maximum (FWHM) of less than 4 Å. The line ratios calculated between Hα and Hβ for SDSSJ222726.64+120539.8 and SDSSJ162753.47+482529.3 were 2.78 and 2.85, respectively, suggesting that the galaxies are starburst galaxies. The measurement of the Hα line from both galaxies was then used to assess the rate of star formation. The star formation rate of the galaxies SDSSJ222726.64+120539.8 and SDSSJ162753.47+482529.3 was found to be 0.010 M☉year-1 and 0.016 M☉year-1, respectively, indicating a low rate of star formation, and the emission line metallicity was derived using the Hα and NII line, which were measured to be 8.23 dex and 8.70 dex, respectively. BIBECHANA 18 (2) (2021) 43-49