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At least 289 records · Page 16

Alignments in the orientation of radio jets from AGN and their host galaxies

It is well established that active galactic nuclei (AGNs) play an important role in the evolution of galaxies. These AGNs can be linked to the accretion processes onto massive black holes and past merger events in their host galaxies, which may lead to different alignments of the jets with respect to the host galaxies. This paper presents a study of the position angle (PA) differences between radio and optical images of radio-AGNs based on the second data release (DR2) of the Low Frequency Array (LOFAR) Two-Meter Sky Survey (LoTSS), theKarl G. JanskyVery Large Array Faint Images of the Radio Sky at Twenty-Centimeters Survey (FIRST), the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, and the Sloan Digital Sky Survey (SDSS). We assessed PA measurement biases in the data and classified the radio-AGNs based on the radio luminosity and infrared colour from the Wide-field Infrared Survey Explorer (WISE). This resulted in the largest sample of radio AGNs with reliable radio and optical PA measurements published to date, with a total of 3682 AGNs. The PA difference (dPA) distributions for the radio-AGN sample show a prominent minor-axis alignment tendency. Based on some simple assumptions, we simulated the projection effect to estimate the intrinsic jet-galaxy alignment. The observed dPA distribution can be well described by a two-component jet-alignment model in which one component is more aligned with the minor axis of the host galaxy than the other. The fitting results indicate that the jet alignment is dependent on radio luminosity and the shape of the host galaxies, with the jets being more likely to be aligned with the minor axis of the galaxy for lower radio luminosity and for optically more elongated radio-AGNs. The minor-axis alignment of the entire sample may suggest a coherent accretion model present in most AGN host galaxies, while a considerable number of luminous radio-AGNs with massive host galaxies might have undergone accretion – according to the chaotic model – or past merger events.

Astronomy & Astrophysics↗

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗

The MIGDAL experiment: Measuring a rare atomic process to aid the search for dark matter

We present the Migdal In Galactic Dark mAtter expLoration (MIGDAL) experiment aiming at the unambiguous observation and study of the so-called Migdal effect induced by fast-neutron scattering. It is hoped that this elusive atomic process can be exploited to enhance the reach of direct dark matter search experiments to lower masses, but it is still lacking experimental confirmation. Our goal is to detect the predicted atomic electron emission which is thought to accompany nuclear scattering with low, but calculable, probability, by deploying an Optical Time Projection Chamber filled with a low-pressure gas based on CF 4 . Initially, pure CF 4 will be used, and then in mixtures containing other elements employed by leading dark matter search technologies — including noble species, plus Si and Ge. High resolution track images generated by a Gas Electron Multiplier stack, together with timing information from scintillation and ionisation readout, will be used for 3D reconstruction of the characteristic event topology expected for this process — an arrangement of two tracks sharing a common vertex, with one belonging to a Migdal electron and the other to a nuclear recoil. Different energy-loss rate distributions along both tracks will be used as a powerful discrimination tool against background events. In this article we present the design of the experiment, informed by extensive particle and track simulations and detailed estimations of signal and background rates. In pure CF 4 we expect to observe 8.9 (29.3) Migdal events per calendar day of exposure to an intense D–D (D–T) neutron generator beam at the NILE facility located at the Rutherford Appleton Laboratory (UK). With our nominal assumptions, 5σ median discovery significance can be achieved in under one day with either generator.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Fabrication and Study of a Concave Crystal Mirror for the KORTES Project

A method for fabrication of a concave spherical mirror made of crystalline quartz is proposed and X-ray optical properties of the mirror are studied. The radius of curvature is 1630 mm. The shape of the mirror surface is studied using an interferometer with a diffraction reference wave. In the entire processing region, the maximum deviation from the nearest sphere is about 0.2 μm and the rms is 34 nm, so that high quality of imaging is provided with an angular error of about 2″ (angular seconds). Reflection coefficient (about 7%) and lower bound limit of spectral selectivity (λ/δλ ≈ 1775) are determined in the vicinity of a wavelength of λ = 0.834 nm, which is close to the working wavelengths of the MgXII doublet (λ = 0.8418 and 0.8423 nm). Position of the Bragg peak and spectral selectivity remain unchanged upon bending of the crystal mirror within measurement accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

36 MATERIALS SCIENCE↗

Shock-induced bubble jets: a dual perspective of bubble collapse and interfacial instability theory

Interactions between shock waves and gas bubbles in a liquid can lead to bubble collapse and high-speed liquid jet formation, relevant to biomedical applications such as shock wave lithotripsy and targeted drug delivery. This study reveals a complex interplay between acceleration-induced instabilities that drive jet formation and radial accelerations causing overall bubble collapse under shock wave pressure. Using high-speed synchrotron X-ray phase contrast imaging, the dynamics of micrometre-sized air bubbles interacting with laser-induced underwater shock waves are visualised. These images offer full optical access to phase discontinuities along the X-ray path, including jet formation, its propagation inside the bubble, and penetration through the distal side. Jet formation from laser-induced shock waves is suggested to be an acceleration-driven process. A model predicting jet speed based on the perturbation growth rate of a single-mode Richtmyer–Meshkov instability shows good agreement with experimental data, despite uncertainties in the jet-driving mechanisms. The jet initially follows a linear growth phase, transitioning into a nonlinear regime as it evolves. To capture this transition, a heuristic model bridging the linear and nonlinear growth phases is introduced, also approximating jet shape as a single-mode instability, again matching experimental observations. Upon piercing the distal bubble surface, jets can entrain gas and form a toroidal secondary bubble. Linear scaling laws are identified for the pinch-off time and volume of the ejected bubble relative to the jet’s Weber number, characterising the balance of inertia and surface tension. At low speeds, jets destabilise due to capillary effects, resulting in ligament pinch-off.

Drops and Bubbles: Bubble dynamics↗

Meta-optic accelerators for object classifiers

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic–based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.

42 ENGINEERING↗

Infrared Imaging Using Thermally Stable HgTe/CdS Nanocrystals

Transferring nanocrystals (NCs) from the laboratory environment toward practical applications has raised new challenges. HgTe appears as the most spectrally tunable infrared colloidal platform. Its low-temperature synthesis reduces the growth energy cost yet also favors sintering. Once coupled to a read-out circuit, the Joule effect aggregates the particles, leading to a poorly defined optical edge and large dark current. Here, in this study, we demonstrate that CdS shells bring the expected thermal stability (no redshift upon annealing, reduced tendency to form amalgams, and preservation of photoconduction after an atomic layer deposition process). The electronic structure of these confined particles is unveiled using k.p self-consistent simulations showing a significant exciton binding energy of ~ 200 meV. After shelling, the material displays a p-type behavior that favors the generation of photoconductive gain. The latter is then used to increase the external quantum efficiency of an infrared imager, which now reaches 40% while presenting long-term stability.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Geostationary Satellite Retrievals with Irradiances and Cloud Properties / Processed Data

The Geostationary Operational Environmental Satellite (GOES) records sky brightness temperatures and irradiances at several wavelengths. From these the cloud microphysical and optical properties can be routinely retrieved. These properties are derived using the technique proposed by Minnis et al. 2020, and is similar to the technique used for deriving cloud properties from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The derived GOES properties for the duration of the second Wind Forecasting Improvement Project (WFIP2) within the domain are reported here. Nominally the retrieved dataset has a 30- minute temporal and a 9 km spatial resolution.

17 WIND ENERGY↗

AGN in dwarf galaxies: frequency, triggering processes and the plausibility of AGN feedback

Abstract While active galactic nuclei (AGN) are considered to be key drivers of the evolution of massive galaxies, their potentially significant role in the dwarf-galaxy regime (M* < 109 M⊙) remains largely unexplored. We combine optical and infrared data, from the Hyper Suprime-Cam (HSC) and the Wide-field Infrared Explorer, respectively, to explore the properties of ∼800 AGN in dwarfs at low redshift (z < 0.3). Infrared-selected AGN fractions are ∼10–30 per cent in dwarfs, which, for reasonable duty cycles, indicates a high black hole (BH)-occupation fraction. Visual inspection of the deep HSC images indicates that the merger fraction in dwarf AGN (∼6 per cent) shows no excess compared to a control sample of non-AGN, suggesting that the AGN-triggering processes are secular in nature. Energetic arguments indicate that, in both dwarfs and massive galaxies, bolometric AGN luminosities (LAGN) are significantly greater than supernova luminosities (LSN). LAGN/LSN is, in fact, higher in dwarfs, with predictions from simulations suggesting that this ratio only increases with redshift. Together with the potentially high BH-occupation fraction, this suggests that if AGN feedback is an important driver of massive-galaxy evolution, the same is likely to be true in the dwarf regime, contrary to our classical thinking.

Kaviraj, Sugata (ORCID:000000025601575X)↗

AGR-2 TRISO Layer Thickness Imaging Archive

As a part of fuel quality control characterization, optical microscopy images of particle cross sections near midplane were acquired at Oak Ridge National Laboratory (ORNL). These particles were produced by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program’s AGR-2 irradiation campaign. These images may be of use for the development of image processing algorithms with the benchmark values measured at ORNL. This report provides those benchmark values, along with the raw images and data generated by the ORNL particle layer thickness analysis process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Connecting Femtosecond Transient Absorption Microscopy with Spatially Coregistered Time Averaged Optical Imaging Modalities

Multimodal all-optical imaging involving coregistered femtosecond transient absorption microscopy (TAM), time-integrated photoluminescence (PL), and steady-state modalities such as confocal reflectance and transmission offers an appealing approach to gain a comprehensive understanding of complex electronic excited-state phenomena in spatially heterogeneous systems. A unique combination of these modalities allows us to unravel not only the competing electronic excited-state dynamical processes but also the underlying morphological information with simultaneous high temporal and spatial resolution. However, correlating the various images obtained from time-resolved and time-independent modalities is generally nontrivial and particularly challenging when the electronic dynamics under study evolve in both time and space. Here, we demonstrate a new approach for rationally correlating time-resolved microscopy with coregistered time-integrated or steady-state modalities. Specifically, our approach involves an extended global lifetime analysis of the time-resolved microscopic data set to separate distinct dynamical processes taking place on commensurate time scales, and the resulting decay-associated amplitude maps (DAAMs) were applied to explore correlations with the images acquired using time-independent modalities. The feasibility of our approach was validated through analyzing a multimodal data set acquired from a thin film of chloride-containing mixed lead halide perovskites (CH 3 NH 3 PbI 3–x Cl x ) using femtosecond transient absorption, time-integrated PL, and confocal reflectance microscopies. Analysis of the results obtained enable us to gain new insight into the complex ultrafast relaxation dynamics in this highly heterogeneous system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Perspectives on Microfluidics for the Study of Asphaltenes in Upstream Hydrocarbon Production: A Minireview

The utilization of microfluidics has generated deep insights into asphaltene precipitation mechanisms and oil–water emulsion stabilization. Agglomeration and precipitation of asphaltenes can cause flow assurance problems during the extraction and transportation of crude oil. Change in temperature, pressure, reservoir conditions, and solvents can change the local environment, leading to asphaltene precipitation. Understanding asphaltene properties and precipitation pathways becomes critical in devising mitigation methods, demulsifiers, and suitable conditions during hydrocarbon processing. Microfluidics has helped in high throughput measurement studies, understanding critical processing conditions, fast demulsifier screening, and the effect of solvent concentration on deposition, generating useful information for utilization at the point of resource extraction facilitating improved resource management. It has become possible to capture the porous, complex nature of reservoir formations and the interaction of chemicals during precipitation through integrated analytics and visualization studies available only through microfluidics. The use of droplet microfluidics, with optical microscopy and high-speed imaging to study the oil–water interface, has resulted in greater understanding of the role of asphaltenes in interfacial properties and emulsion stabilization. Here, this minireview highlights the crucial aspects of microfluidics that have been used to understand physicochemical behavior and dynamics of asphaltene deposition. Some of the unique devices have been presented focusing on the key elements of microfluidics design, fabrication, and analysis, as the insight obtained from microfluidics strongly depends on the device design and the controllability of the experimental parameters. Successful implementation of microfluidics for efficient and controlled experiments, short analysis time scales and rapid screening, and generation of high-quality, reliable data that convey asphaltene deposition issues and interface behavior in emulsions shows the importance of microsystems for advancing knowledge in hydrocarbon production and processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

In-cylinder spray evolution in a motored central-injection gasoline engine: Imaging and simulating the effects of flash-boiling and intake crossflow

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. Further, it is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models’ constants.

33 ADVANCED PROPULSION SYSTEMS↗

Using block-copolymer nanolithography as a tool to sensitively evaluate variation in chemical dry etching rates of semiconductor materials with sub-5 nm resolution

Ion implantation is a robust and established method to customize the electronic properties of Si. However, fabricating doped, ultrafine semiconductor nanostructures can be challenging. Ion implantation has well-established effects on the dry etch rates of Si, which becomes increasingly consequential as the target dimension shrinks below a few tens of nanometers. While dry etching arrays of block copolymer-templated nanoscale holes (pitch = 37.5 nm, diameter ~25 nm) into p-type, n-type, and undoped Si, we observed that the lateral etch rate was notably larger for the n-type regions than p-type or undoped regions. By doing image analyses on high resolution electron micrographs of the nanostructured hole arrays, we were able to extract the porosity and average radii of the holes with subnanometer sensitivity and compare the relative etch rates between different doping conditions. We found that degenerately doped n-type silicon consistently etches between approximately 17% and 27% faster in the lateral direction than p-type Si, resulting in significantly larger porosity and, consequently, less mechanical stability. Here, we demonstrate that top-down dimensional analysis of a densely packed porous nanostructure is a robust method for assessing extremely small differences in the lateral, chemical etch rate of doped Si to a degree of sensitivity that was previously unachievable. The minute, dense-packed nature of block copolymer self-assembled nanostructures is shown to be ideal for this application. This proposed method could be useful for designing fabrication processes for heterogeneous nanostructures, as slight dry etch rate variations that may be within process tolerance at the micrometer-scale appear to have nontrivial consequences at the nanometer scale.

42 ENGINEERING↗

Polarization Control via Artificial Optical Nonlinearity in Dielectric Metasurfaces

Nonlinear optical phenomena are generally governed by geometry in matter systems, as they depend on the spatial arrangement of atoms within materials or molecules. Metasurfaces, through precisely designed geometries on a subwavelength scale, allow the optical response of a material to be tailored far beyond its natural properties. Therefore, metasurfaces are highly appealing for enabling the engineering of nonlinear optical interactions. Current studies of nonlinear metasurfaces predominantly focus on the phase control of the generated light. Nonetheless, investigating the tensorial nature of the nonlinearity of metasurfaces and its effect on the polarization of the generated light is critical to fully unlocking a range of applications, such as nonlinear vector beam generation and nonlinear polarization imaging. Here, we study the artificial optical nonlinearity of a dielectric metasurface originating from its meta-atom symmetry and describe the third-order nonlinear behavior by considering the polarization degree of freedom. We establish an effective nonlinear medium model that serves as a design toolbox for developing amorphous silicon-based geometric metasurfaces with customizable features for third-harmonic generation. We further extract quantitative values of the artificial nonlinear susceptibility tensor elements related to the investigated nonlinear process and geometry. The implemented functional devices demonstrate the versatility of dielectric metasurfaces in shaping the emitted light in terms of amplitude, phase, and polarization for the precise engineering of advanced nonlinear architectures targeting applications in nonlinear imaging and complex light generation.

Diffraction↗

Spiking retina microscope

A spiking retina microscope comprising microscope optics and a neuromorphic imaging sensor. The microscope optics are configured to direct a magnified image of a specimen onto the neuromorphic imaging sensor. The neuromorphic imaging sensor comprises a plurality of sensor elements that are configured to generate spike signals in response to integrated light from the magnified image reaching a threshold. The spike signals may be processed by a processor unit to generate a result, such as tracking biological particles in a specimen comprising biological material.

Severa, William Mark↗

Evolution of Pore Structure and Permeability of Rocks Under Hydrothermal Conditions (Final Report)

The physical and transport properties of porous rocks can be altered by a variety of diagenetic, metamorphic, and tectonic processes, and the changes that result are of critical importance to such industrial applications as resource recovery, carbon dioxide sequestration, and waste isolation in geologic formations. The associated interrelationships between rocks, pore fluids and deformation are also key to understanding many natural processes, including dynamic metamorphism, fault mechanics, fault stability and pressure solution creep. In this project, we investigated the changes of permeability and pore geometry owing to inelastic deformation by solution-transfer, brittle fracturing and dislocation creep. In particular, we studied the coupling between pore fluids and deformation in fluid-filled quartz, calcite, mudstones and ultramafic rocks and examine the effects of coupled mechanical and chemical processes on the evolution of porosity and permeability under hydrothermal conditions. The investigations used a combination of techniques, that included triaxial laboratory experiments; uniaxial compressive loading with in situ observations of microstructure; permeability under hydrostatic compression; observations of microstructure using optical and electron microscopes, and micro-CT imaging; and numerical calculations. Laboratory experiments were designed to provide mechanical and transport data under conditions that isolate the particular mechanisms responsible for the changes. The data obtained were used to quantify changes in surface roughness, porosity, pore dimensions and their spatial fluctuations. The results of the experiments and data from image analyses were compared to the results of network, finite-difference and other numerical models to verify the validity of experimentally established relations between permeability and other rock properties. A bibliography for the period from 2015-2019 is appended below. New results obtained in the final year and the unfunded extension period are detailed below.

58 GEOSCIENCES↗