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At least 901 records · Page 50

Advances in utilizing event based data structures for neutron scattering experiments

This article strives to expand on existing work to demonstrate advancements in data processing made available using event mode measurements. Most spallation neutron sources in the world have data acquisition systems that provide event recording. The new science that is enabled by utilizing event mode has only begun to be explored. In the past, these studies were difficult to perform because histograms forced dealing with either large chunks of time or a large number of files. With event based data collection, data can be explored and rebinned long after the measurement has completed. This article will review some of the principles of event data and how the method opens up new possibilities for in situ measurements, highlighting techniques that can be used to explore changes in the data. We also demonstrate the statistical basis for determining data quality and address the challenge of determining how long to measure mid-measurement. Lastly, we demonstrate a model independent method of grouping data via hierarchical clustering methods that can be used to improve calibration, reduction, and data exploration.

74 ATOMIC AND MOLECULAR PHYSICS↗

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗

ERRSAC contributions to the search for Appalachian hydrocarbons

The utility of lineaments identified on LANDSAT imagery as an exploration tool in the search for hydrocarbons within three Appalachian test sites were assessed. The optimum LANDSAT imagery enhancement technique for displaying lineaments was identified. The LANDSAT lineament data were analyzed and results were correlated with oil and gas field information for each of three tests sites. Good correlations were found for several states. Successful techniques can be incorporated into a broader exploration model.

Blodget, H. W.↗

An open retail boundary dataset for South Korea using open data and computer vision technique

Although delineating retail boundaries is important to explore and comprehend the dynamics of the retail sector, it is hard to find studies specifically addressing it in the South Korean context. This study fills this gap by proposing new retail boundaries across South Korea. To achieve this goal, we employed a variety of retailers and building datasets and proposed a unique computer vision-based framework with a deep ensemble voting technique. As a result, we delineated 6,636 distinct retail boundaries that were validated against existing reference retail boundaries. These newly delineated retail boundaries provide valuable insights for researchers, governments, and other relevant stakeholders by enhancing their understanding of retail geography. This dataset can be used as a foundational resource for analyses on topics such as pandemic recovery, retail gentrification, and the resilience of retail spaces in response to e-commerce growth, ultimately contributing to more robust retail sector research in South Korea.

97 MATHEMATICS AND COMPUTING↗

Crystallographic map: A general lattice and basis formalism enabling efficient and discretized exploration of crystallographic phase space

Three-dimensional lattices are fundamental to solid-state physics. The description of a lattice with an atomic basis constitutes the necessary information to predict solid phase properties and evolution. Here, we present an algorithm for systematically exploring crystallographic phase space. Further, coupled with ab initio techniques, such as density functional theory, this algorithm offers an approach for exploring and tuning materials behavior, with a broad range of potential applications: particularly martensitic phase transformations and materials stability.

36 MATERIALS SCIENCE↗

When not to use machine learning: A perspective on potential and limitations

Abstract The unparalleled success of artificial intelligence (AI) in the technology sector has catalyzed an enormous amount of research in the scientific community. It has proven to be a powerful tool, but as with any rapidly developing field, the deluge of information can be overwhelming, confusing, and sometimes misleading. This can make it easy to become lost in the same hype cycles that have historically ended in the periods of scarce funding and depleted expectations known as AI winters. Furthermore, although the importance of innovative, high-risk research cannot be overstated, it is also imperative to understand the fundamental limits of available techniques, especially in young fields where the rules appear to be constantly rewritten and as the likelihood of application to high-stakes scenarios increases. In this article, we highlight the guiding principles of data-driven modeling, how these principles imbue models with almost magical predictive power, and how they also impose limitations on the scope of problems they can address. Particularly, understanding when not to use data-driven techniques, such as machine learning, is not something commonly explored, but is just as important as knowing how to apply the techniques properly. We hope that the discussion to follow provides researchers throughout the sciences with a better understanding of when said techniques are appropriate, the pitfalls to watch for, and most importantly, the confidence to leverage the power they can provide. Graphical abstract

36 MATERIALS SCIENCE↗

Energy Storage and Generation for Extreme Temperature and Pressure and Directional Measurement While Drilling Applications

FastCAP Systems Corporation has successfully completed all milestones defined by the award DE-EE0005503. Under this program, FastCAP developed three critical subassemblies to TRL3 demonstrating proof of concept of a geothermal MWD power source. This power source includes an energy harvester, electronics and a novel high temperature ultracapacitor (“ultracap”) rechargeable energy storage device suitable for geothermal exploration applications. FastCAP’s ruggedized ultracapacitor (ultracap) technology has been proven and commercialized in oil and gas exploration operating to rated temperatures of 150°C. Characteristics of this technology are that it is rechargeable and relatively high power. This technology was the basis for the advancements in rechargeable energy storage under this project. The ultracap performs reliably at 250°C and beyond and operates over a wide operating temperature range: -5°C to 250°C. The ultracap has significantly higher power density than lithium thionyl chloride batteries, a non-rechargeable incumbent used in oil and gas drilling today. Several hermetically sealed, prototype devices were tested in our laboratories at constant temperatures of 250°C showing no significant degradation over 2000 hours of operation. Other prototypes were tested at Sandia National Lab in the month of April, 2015 for a third party performance validation. These devices showed outstanding performance over 1000 hours of operation at three rated temperatures, 200°C, 225°C and 250°C, with negligible capacitance degradation and minimal equivalent series resistance (ESR) increase. Similarly, FastCAP’s ruggedized electronics have been proven and commercialized in oil and gas exploration operating to rated temperatures of 150°C. This technology was the basis for the advancements in downhole electronics under this project. Principal contributions here focused on design for manufacture innovations that have reduced the prototype build cycle time by a factor of 10x. The electronics have demonstrated a substantially reduced design cycle time by way of process and material selection innovations and have been qualified for 250°C / 10 Grms for at least 200 hours. FastCAP has also invented a rotary inertial energy generator (RIEG) to harvest various mechanical energy sources that exist downhole. This device is flow-independent and has been demonstrated as a proof of concept to survive geothermal well temperatures under this project. The herein energy harvester has been developed to provide operational power by harvesting rotational mechanical energy that exists downhole in geothermal drilling. The energy harvester has been tested at 250°C / 10 Grms for 200 hours. Deployment of these technologies in geothermal drilling and exploration applications could have an immediate and significant impact on the effectiveness and efficiency of drilling processes, particularly with regard to use of advanced logging and monitoring techniques. The ultimate goal of this work is to reduce drilling risk to make geothermal energy more attractive and viable to the customer. Generally speaking, we aim to support the transfer of MWD techniques from oil and gas to geothermal exploration with considerations toward the practical differences between the two. One of the most significant obstacles to the deployment of advanced drilling and production techniques in the geothermal context are limitations related to the maximum operating temperatures of downhole batteries used to provide power for downhole sensors, steering tools, telemetry equipment and other MWD/LWD technologies. FastCAP’s higher temperature ultracapacitor technology will provide power solutions for similar advanced drilling and production techniques, even in the harsher environments associated with geothermal energy production. This ultracapacitor will enable downhole power solutions for the geothermal industry capable of the same reliable and safe operation our team has demonstrated in the oil and gas context. Without batteries, geothermal MWD is left without a downhole power source. Some very high temperature turbines exist but provide unsteady, intermittent power and no power when the flow is off. In high loss formations common to geothermal exploration, it will be auspicious to support air drilling in which case there is no flow to power a turbine at all. In the best case, rechargeable energy storage will help to buffer unsteady power from non-battery power sources and in the worst case it will be needed to store energy from highly intermittent sources to provide a continuously operable power source to the tool.

15 GEOTHERMAL ENERGY↗

magnetoARPES: Angle Resolved Photoemission Spectroscopy with magnetic field control

Angle-Resolved Photoemission Spectroscopy (ARPES) is a premier technique for understanding the electronic excitations in conductive, crystalline matter, in which the induced photocurrent is collected and dispersed in energy and angle of emission to reveal the energy- and momentum-dependent single particle spectral function A (k,ω). So far, ARPES in a magnetic field has been precluded due to the need to preserve the electron paths between the sample and detector. Here, in this paper we report progress towards “magnetoARPES”, a variant of ARPES that can be conducted in a magnetic field. It is achieved by applying a microscopic probe beam (≲10 μm) to a thinned sample mounted upon a special sample holder that generates magnetic field confined to a thin layer near the sample surface. In this geometry we could produce ARPES in magnetic fields up to around ±100 mT. The magnetic fields can be varied from purely in-plane to nearly purely out-of-plane, by scanning the probe beam across different parts of the device. We present experimental and simulated data for graphene to explore the aberrations induced by the magnetic field. These results demonstrate the viability of the magnetoARPES technique for exploring symmetry breaking effects in weak magnetic fields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Height reconstruction techniques for synthetic aperture lidar systems

The data-processing techniques and acquisition modes of a synthetic aperture lidar (SAL) instrument operating at optical wavelengths are closely related to the analogous modes of a synthetic aperture radar (SAR) instrument operating at microwave frequencies. It is consequently natural to explore the applicability of SAR processing techniques to SAL sensors. In this paper, we examine the feasibility of adopting SAR height-reconstruction techniques with SAL sensors to obtain high-resolution 3-D imagery at optical wavelengths.

synthetic aperture lidar (SAL)↗

Reaction dynamics and the interstellar environment

Following a brief outline of the 'normal' equilibrium reaction rate laws, the theme of thermal disequilibrium in interstellar space and the related topic of detailed rate constants are more extensively discussed. Comment is made concerning the two principal techniques that are currently being used to explore the dynamical details of an increasing range of chemical reactions in the laboratory, since it is considered that these techniques suggest ways in which the understanding of the chemistry of interstellar space may be extended.

Polanyi, J. C.↗

The 3D widgets for exploratory scientific visualization

Computational fluid dynamics (CFD) techniques are used to simulate flows of fluids like air or water around such objects as airplanes and automobiles. These techniques usually generate very large amounts of numerical data which are difficult to understand without using graphical scientific visualization techniques. There are a number of commercial scientific visualization applications available today which allow scientists to control visualization tools via textual and/or 2D user interfaces. However, these user interfaces are often difficult to use. We believe that 3D direct-manipulation techniques for interactively controlling visualization tools will provide opportunities for powerful and useful interfaces with which scientists can more effectively explore their datasets. A few systems have been developed which use these techniques. In this paper, we will present a variety of 3D interaction techniques for manipulating parameters of visualization tools used to explore CFD datasets, and discuss in detail various techniques for positioning tools in a 3D scene.

Herndon, Kenneth P.↗

Exploring quantum statistics for massive Dirac and Majorana neutrinos using spinor-helicity techniques

Recently, there has been interest in the applicability of quantum statistics to distinguish Dirac from Majorana neutrinos in multineutrino final states. In particular, debate has arisen over the validity of the Dirac-Majorana confusion theorem in these processes, i.e., that any distinction between the Dirac and Majorana processes goes to zero as the neutrino mass goes to zero. Here we approach this problem equipped with spinor-helicity methods generalized for massive Dirac and Majorana fermions. We explicitly calculate all helicity amplitudes, and their squares, for the decay of a light scalar particle to two neutrinos and two oppositely charged leptons. This allows us to pinpoint the crucial steps which could lead to claims of a violation of the confusion theorem. We show that, if the correct antisymmetrization of Dirac to Majorana amplitudes is used, identification of which is clear in this framework, and all relevant contributions are appropriately summed, a scalar decay into two charged leptons and two neutrinos satisfies the Dirac-Majorana confusion theorem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Conference on Complex Turbulent Flows: Comparison of Computation and Experiment, Stanford University, Stanford, CA, September 14-18, 1981, Proceedings. Volume 2 - Taxonomies, reporters' summaries, evaluation, and conclusions

Computational techniques for simulating turbulent flows were explored, together with the results of experimental investigations. Particular attention was devoted to the possibility of defining a universal closure model, applicable for all turbulence situations; however, conclusions were drawn that zonal models, describing localized structures, were the most promising techniques to date. The taxonomy of turbulent flows was summarized, as were algebraic, differential, integral, and partial differential methods for numerical depiction of turbulent flows. Numerous comparisons of theoretically predicted and experimentally obtained data for wall pressure distributions, velocity profiles, turbulent kinetic energy profiles, Reynolds shear stress profiles, and flows around transonic airfoils were presented. Simplifying techniques for reducing the necessary computational time for modeling complex flowfields were surveyed, together with the industrial requirements and applications of computational fluid dynamics techniques.

Kline, S. J.↗

A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS) techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression. Also, we provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Finally, our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.

97 MATHEMATICS AND COMPUTING↗

SISGR: Atom chip microscopy: A novel probe for strongly correlated materials

Microscopy techniques co-opted from nonlinear optics and high energy physics have complemented solid-state probes in elucidating the order manifest in condensed matter materials. Up until now, however, no attempts have been made to use modern techniques of ultracold atomic physics to directly explore properties of strongly correlated or topologically protected materials. Our current program is focused on introducing a novel magnetic field microscopy technique into the toolbox of imaging probes. Our prior DOE ESPM program funded the development of a novel instrument using a dilute gas Bose-Einstein condensate (BEC) as a scanning probe capable of measuring tiny magnetic (and electric) DC and AC fields above materials. We successfully built the world's first “scanning cryogenic atom chip microscope”, and we now are in the process of characterizing its performance before using the instrument to take the first wide-area images of transport flow within unconventional superconductors, pnictides and oxide interfaces (LAO/STO), topological insulators, and colossal magnetoresistive manganites. We will do so at temperatures outside the capability of scanning SQUIDs, with ~10x better resolution and without 1/f-noise. A notable goal will be to measure the surface-to-bulk conductivity ratio in topological insulators in a relatively model-independent fashion. We have completed the construction of this magnetic microscope. The instrument uses atom chips—substrates supporting micron-sized current-carrying wires that create magnetic microtraps near surfaces for ultracold thermal gases and BECs—to enable single-shot and raster-scanned large-field-of-view detection of magnetic fields. The fields emanating from electronic transport may be detected at the 10 -7 flux quantum (Φ0) level and below; that is, few to sub-micron resolution of sub-nanotesla fields over single-shot, millimeter-long detection lengths. By harnessing the extreme sensitivity of atomic clocks and BECs to external perturbations, we are now in a position to use atom chips for imaging transport in new regimes. Scanning quantum gas atom chip microscopy introduces three very important features to the toolbox of high-resolution scanning microscopy of strongly correlated or topological materials: simultaneous detection of magnetic and electric fields (down to the sub-single electron charge level; no invasive large magnetic fields or gradients; simultaneous micro- and macroscopic spatial resolution; DC to MHz detection bandwidth; freedom from 1/ƒ flicker noise at low frequencies; and, perhaps most importantly, the complete decoupling of probe and sample temperatures. The atom chip microscope can operate at maximum sensitivity and resolution without regard to the substrate temperature. While the BEC is among the coldest objects realizable (100 nK temperatures are typical), the atom chip substrate can be positioned 1 μm away from the BEC and be as hot as 400 K or as cold as the cryostat can cool. This is because unlike superconducting probes, whose temperature is closely coupled to nearby materials, quantum gases are immune to radiative heating. The energy gap between a Rb atom’s ground state and first excited state far exceeds the typical energy of room-temperature blackbody radiation; such atoms are therefore transparent to radiation heating by materials at room temperature or below. We experimentally demonstrated a new atom chip trapping system that allows the placement and high-resolution imaging of ultracold atoms within microns from any ≤100 μm-thin, UHV-compatible material, while also allowing sample exchange with minimal experimental downtime. The sample is not connected to the atom chip, allowing rapid exchange without perturbing the atom chip or laser cooling apparatus. Exchange of the sample and retrapping of atoms has been performed within a week turnaround, limited only by chamber baking. Moreover, the decoupling of sample and atom chip provides the ability to independently tune the sample temperature and its position with respect to the trapped ultracold gas, which itself may remain in the focus of a high-resolution imaging system. We confine 100-nK BECs of 104 87Rb atoms near a gold-mirrored 100-μm-thick silicon substrate. The substrate can be cooled to 35 K without use of a heat shield, while the atom chip, 120-μm away, remains at room temperature. Atoms may be imaged with 1-μm resolution and retrapped every 16 s, allowing rapid data collection. Straightforward improvements will allow us to push sample temperatures close to 4 K, and improve imaging resolution from 1 μm down to a few-100 nm, thereby providing 10 -9 Φ0 detection sensitivity. We will test the utility of this technique by imaging the magnetic fields emanating from electronic transport and domain percolation in several interesting examples of strongly correlated or topologically protected materials. STM, transport, and x-ray scattering experiments have, among others, revealed the existence of a quantum liquid crystal state in iron (pnictide) and cuprate superconductors. This strongly correlated state of matter could also be detected by imaging the fluctuating transport (spatially and in time) of electrons as the phase/regime boundary is crossed between the pnictide non-Fermi liquid (cuprate strange metal) and the pnictide magnetic phase (cuprate pseudogap regime). Our ability to image wide-area inhomogeneous current flow from room-temperature to <10 K will allow us to study the developing domain structure and transport near twin boundary interfaces through the T N ≈50–150 K nematic transition recently identified in bulk transport experiments by Ian Fisher's group in underdoped Fe-arsinide superconductors. Again, this highlights a main feature of our cryogenic atom chip microscope: the ability to image transport regardless of the sample temperature since the BEC, at nK temperatures, is transparent to blackbody radiation, even when held a microns from the surface.

36 MATERIALS SCIENCE↗

1/f noise measurements for faster evaluation of electromigration in advanced microelectronics interconnections

The use of 1/f noise measurements is explored for the purpose of finding faster techniques for electromigration (EM) characterization in advanced microelectronic interconnects, which also enable a better understanding of its underlying physical mechanisms. Three different applications of 1/f noise for EM characterization are explored. First, whether 1/f noise measurements during EM stress can serve as an early indicator of EM damage. Second, whether the current dependence of the noise power spectral density (PSD) can be used for a qualitative comparison of the defect concentration of different interconnects and consequently also their EM lifetime t50. Third, whether the activation energies obtained from the temperature dependence of the 1/f noise PSD correspond to the activation energies found by means of classic EM tests. In this paper, the 1/f noise technique has been used to assess and compare the EM properties of various advanced integration schemes and different materials, as they are being explored by the industry to enable advanced interconnect scaling. More concrete, different types of copper interconnects and one type of tungsten interconnect are compared. The 1/f noise measurements confirm the excellent electromigration properties of tungsten and demonstrate a dependence of the EM failure mechanism on copper grain size and distribution, where grain boundary diffusion is found to be a dominant failure mechanism.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗