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At least 739 records · Page 41

Multichannel Analysis of Surface Waves Accelerated (MASWAccelerated): Software for efficient surface wave inversion using MPI and GPUs

Multichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer 1D layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics processing units (GPUs). We propose new parallel and GPU accelerated algorithms for computing 1D MASW inversion, and provide software implementations in C using Message Passing Interface (MPI) and CUDA. These algorithms take advantage of sparsity that arises in the problem, and the work balance between processes considers typical data trends. We compare our methods to an existing open source Matlab MASW tool. Our serial C implementation achieves a 2x speedup over the Matlab software, and we continue to see improvements by parallelizing the problem with MPI. Here we see nearly perfect strong and weak scaling for uniform data, and improve strong scaling for realistic data by repartitioning the problem to process mapping. By utilizing GPUs available on most modern workstations, we observe an additional 1.3x speedup over the serial C implementation on the first use of the method. We typically repeatedly evaluate theoretical dispersion curves as part of an optimization procedure, and on the GPU the kernel can be cached for faster reuse on later runs. We observe a 3.2x speedup on the cached GPU runs compared to the serial C runs. This work is the first open-source parallel or GPU-accelerated software tool for MASW imaging, and should enable geotechnical engineers to fully utilize all computer hardware at their disposal.

58 GEOSCIENCES↗

Low-rank separated representation surrogates of high-dimensional stochastic functions: Application in Bayesian inference

This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.

97 MATHEMATICS AND COMPUTING↗

Explainable machine learning of the underlying physics of high-energy particle collisions

We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the final state particles. We demonstrate the proof-of-concept of our White Box AI approach using a Generative Adversarial Network (GAN) which learns from a DGLAP-based parton shower Monte Carlo event generator. The constrained generator network architecture mimics the structure of a parton shower exhibiting similarities with Recurrent Neural Networks (RNNs). We show, for the first time, that our approach leads to a network that is able to learn not only the final distribution of particles, but also the underlying parton branching mechanism, i.e. the Altarelli-Parisi splitting function, the ordering variable of the shower, and the scaling behavior. While the current work is focused on perturbative physics of the parton shower, we foresee a broad range of applications of our framework to areas that are currently difficult to address from first principles in QCD. Examples include nonperturbative and collective effects, factorization breaking and the modification of the parton shower in heavy-ion, and electron-nucleus collisions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Fragmentation of the edge of a terminated Cu nanolayer within a Nb matrix upon annealing

Here, we investigate the evolution of the tip of a terminated Cu nano-layer embedded within a Nb matrix upon annealing at 800°C. The tip of the terminated Cu layer fragments progressively into a series of single-crystal Cu particles upon continued annealing. This finding shows that layer fragmentation is the second step, after layer pinchoff, in the thermal coarsening of nanolayered metal composites. Comparison to prior phase-field modeling permits us to infer that the mobility at 800°C of Cu-Nb, as modeled by the Cahn-Hillard equation, is M(c)=(M 0 = 7.3 ± 3.6 nm 5 / s • eV) |1 – c 2 |, where the order parameter, c, is 1 for Cu and -1 for Nb.

36 MATERIALS SCIENCE↗

Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive

Three machine learning techniques (multilayer perceptron, random forest and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modelling and real-time control applications. The machine learning models use a database of more than 16 000 GENRAY/CQL3D simulations for training, validation and testing. Latin hypercube sampling methods ensure that the database covers the range of nine input parameters ( $n_{e0}$ , $T_{e0}$ , $I_p$ , $B_t$ , $R_0$ , $n_{\|}$ , $Z_{{\rm eff}}$ , $V_{{\rm loop}}$ and $P_{{\rm LHCD}}$ ) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to $\sim$ ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Effects of Localized Thermal Pressure on Equilibrium Magnetic Fields and Particle Drifts in The Inner Magnetosphere

Localized injections of hot anisotropic plasma sheet particles into the inner magnetosphere can significantly deform the quiet time dipole–like magnetic field and thus disturb electron and ion's drift paths and scattering rates. Although many details of magnetic field deformation can be inferred from empirical models, roles of different characteristics of injected plasma on the structure of such deformation require further investigation. In this study, we use the 2–D axisymmetric equilibrium model to calculate self–consistent magnetic field in force balance with a Gaussian thermal pressure distribution characterized by four input parameters: the ratio between plasma pressure and magnetic pressure ( β ) at the pressure peak β 0 , the radial location of the pressure peak L 0 , the width of the half peak pressure σ 0 , and the equatorial pressure anisotropy A e . Using the modeled magnetic field, we find that the magnetic field perturbation increases with increasing β 0 and decreasing σ 0 while the magnetic curvature perturbation increases with increasing A e , β 0 , and σ 0 and decreasing L 0 . For energetic particles the change of magnetic gradient drift motion is much greater than that of curvature drift motion. The magnetic dip structure formation requires a critical β value that increases with increasing σ 0 and decreasing L 0 . Despite the unavailability of observations in the existing literatures to check the condition of magnetic dip formation, such condition will be checked against observations as a future study. Lastly, we also use 3–D ring current–atmosphere interactions model with self–consistent magnetic field model to illustrate the effect of azimuthal pressure distribution, which is relevant to asymmetric ring current.

79 ASTRONOMY AND ASTROPHYSICS↗

Observational Evidence of State‐Dependent Positive and Negative Land Surface Feedback on Afternoon Deep Convection Over the Southern Great Plains

Abstract The detection of soil moisture‐precipitation feedback is complicated by the dependence of feedback sign on atmospheric state and cloud types. We evaluated responses of convective clouds to changes in surface evaporative fraction (EF) from satellite and ground‐based measurements over the Southern Great Plains in summer months. Strong responses to EF were inferred only after accounting for tropospheric states supporting systematically positive or negative responses. State dependence (inferred from controlled model experiments) explains the weak response found in previous studies. The probability of transitions from shallow to deep cumulus, and subsequent precipitation onset, increases by 58% over drier surfaces in weakly stratified environments having deeper boundary layers. Boundary layer dynamics play a more important role than low‐level relative humidity in these environments. Wetter surfaces yield increased deep convection in moist and thermally stratified environments. The distinct pathways of convective initiation over dry and wet land surfaces can play important roles in drought onset and termination.

Qiu, Shaoyue↗

A Robust Estimate of Continental-Scale Terrestrial Carbon Sinks Using GOSAT XCO 2 Retrievals

Satellite XCO 2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO 2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO 2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO 2 can only promise a robust continental-scale NEE estimate and improving the XCO 2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

58 GEOSCIENCES↗

Bioinformatic and experimental evidence for suicidal and catalytic plant THI4s

Like fungi and some prokaryotes, plants use a thiazole synthase (THI4) to make the thiazole precursor of thiamin. Fungal THI4s are suicide enzymes that destroy an essential active-site Cys residue to obtain the sulfur atom needed for thiazole formation. In contrast, certain prokaryotic THI4s have no active-site Cys, use sulfide as sulfur donor, and are truly catalytic. The presence of a conserved active-site Cys in plant THI4s and other indirect evidence implies that they are suicidal. To confirm this, we complemented the Arabidopsistz-1 mutant, which lacks THI4 activity, with a His-tagged Arabidopsis THI4 construct. LC–MS analysis of tryptic peptides of the THI4 extracted from leaves showed that the active-site Cys was predominantly in desulfurated form, consistent with THI4 having a suicide mechanism in planta. Unexpectedly, transcriptome data mining and deep proteome profiling showed that barley, wheat, and oat have both a widely expressed canonical THI4 with an active-site Cys, and a THI4-like paralog (non-Cys THI4) that has no active-site Cys and is the major type of THI4 in developing grains. Transcriptomic evidence also indicated that barley, wheat, and oat grains synthesize thiamin de novo, implying that their non-Cys THI4s synthesize thiazole. Structure modeling supported this inference, as did demonstration that non-Cys THI4s have significant capacity to complement thiazole auxotrophy in Escherichia coli. There is thus a prima facie case that non-Cys cereal THI4s, like their prokaryotic counterparts, are catalytic thiazole synthases. Bioenergetic calculations show that, relative to suicide THI4s, such enzymes could save substantial energy during the grain-filling period.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An ANFIS-based on B2C electronic commerce transaction

The purpose of this study is to use an adaptive-network-based fuzzy inference system to model a fuzzy logic-based system (FIS) for supporting decision-making process in B2C electronic commerce transaction. Firstly we introduce FIS in B2C electronic commerce transaction and ANFIS. Then we use ANFIS to model FIS with different membership functions(MF). Lastly we give a conclusion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The age and mass of the α Herculis triple-star system from a mesa grid of rotating stars with 1.3 ≤ M/M {sub ☉} ≤ 8.0

α{sup 1} Her is the second closest asymptotic giant branch (AGB) star to the Sun, and the variable luminous M5 Ib-II member of a triple-stellar system containing G8 III and A9 IV-V components. However, the mass of this important star was previously uncertain, with published values ranging from ∼2-15 M {sub ☉}. As shown by this study, its fortuitous membership in a nearby resolved triple-star system makes it possible to determine its fundamental properties including its mass and age. We present over 20 years of VRI photometry of α{sup 1} Her as well as Wing intermediate-band near-IR TiO and NIR continuum photometry. We introduce a new photometry-based calibration technique and extract the effective temperature and luminosity of α{sup 1} Her, in agreement with recent interferometric measures. We find average values of T {sub eff} = 3280 ± 87 K and log (L/L {sub ☉}) = 3.92 ± 0.14. With the MESA code, we calculate a dense grid of evolutionary tracks for Galactic low- to intermediate-mass (1.3 to 8 M {sub ☉}) rotating stars from the pre-main sequence phase to the advanced AGB phase. We include atomic diffusion and rotation mechanisms to treat the effects of extra elemental mixing. Based on the observed properties of the α Herculis stars, we constrain the age of the system to lie in the range 0.41 to 1.25 Gyr. Thus, the mass of α{sup 1} Her lies in the range 2.175 ≤ M/M {sub ☉} ≤ 3.250. We compare our model-based age inference with recent tracks of the Geneva and STAREVOL codes, and show their agreement. In the prescribed mass range for α{sup 1} Her, the observed {sup 12}C/{sup 13}C and {sup 16}O/{sup 17}O ratios are consistent (within 2σ) with the ratios predicted by the MESA, Geneva, and STAREVOL codes.

79 ASTRONOMY AND ASTROPHYSICS↗

IN SITU ACCRETION OF HYDROGEN-RICH ATMOSPHERES ON SHORT-PERIOD SUPER-EARTHS: IMPLICATIONS FOR THE KEPLER-11 PLANETS

Motivated by recent discoveries of low-density super-Earths with short orbital periods, we have investigated in situ accretion of H-He atmospheres on rocky bodies embedded in dissipating warm disks, by simulating quasi-static evolution of atmospheres that connect to the ambient disk. We have found that the atmospheric evolution has two distinctly different outcomes, depending on the rocky body's mass: while the atmospheres on massive rocky bodies undergo runaway disk-gas accretion, those on light rocky bodies undergo significant erosion during disk dispersal. In the atmospheric erosion, the heat content of the rocky body that was previously neglected plays an important role. We have also realized that the atmospheric mass is rather sensitive to disk temperature in the mass range of interest in this study. Our theory is applied to recently detected super-Earths orbiting Kepler-11 to examine the possibility that the planets are rock-dominated ones with relatively thick H-He atmospheres. The application suggests that the in situ formation of the relatively thick H-He atmospheres inferred by structure modeling is possible only under restricted conditions, namely, relatively slow disk dissipation and/or cool environments. This study demonstrates that low-density super-Earths provide important clues to understanding of planetary accretion and disk evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

GRB060218 AS A TIDAL DISRUPTION OF A WHITE DWARF BY AN INTERMEDIATE-MASS BLACK HOLE

The highly unusual pair of a gamma-ray burst (GRB) GRB060218 and an associated supernova, SN2006aj, has puzzled theorists for years. A supernova shock breakout and a jet from a newborn stellar mass compact object have been proposed to explain this pair's multiwavelength signature. Alternatively, we propose that the source is naturally explained by another channel: the tidal disruption of a white dwarf (WD) by an intermediate-mass black hole (IMBH). This tidal disruption is accompanied by a tidal pinching, which leads to the ignition of a WD and a supernova. Some debris falls back onto the IMBH, forms a disk, which quickly amplifies the magnetic field, and launches a jet. We successfully fit soft X-ray spectra with the Comptonized blackbody emission from a jet photosphere. The optical/UV emission is consistent with self-absorbed synchrotron emission from the expanding jet front. The temporal dependence of the accretion rate M-dot (t) in a tidal disruption provides a good fit to the soft X-ray light curve. The IMBH mass is found to be about 10{sup 4} M{sub Sun} in three independent estimates: (1) fitting the tidal disruption M-dot (t) to the soft X-ray light curve, (2) computing the jet base radius in a jet photospheric emission model, and (3) inferring the mass of the central black hole based on the host dwarf galaxy's stellar mass. The position of the supernova is consistent with the center of the host galaxy, while the low supernova ejecta mass is consistent with that of a WD. The high expected rate of tidal disruptions in dwarf galaxies is consistent with one source observed by the Swift satellite over several years at a distance of 150 Mpc measured for GRB060218. Encounters with WDs provide much fuel for the growth of IMBHs.

79 ASTRONOMY AND ASTROPHYSICS↗

EARLIEST STAGES OF PROTOCLUSTER FORMATION: SUBSTRUCTURE AND KINEMATICS OF STARLESS CORES IN ORION

We study the structure and kinematics of nine 0.1 pc scale cores in Orion with the IRAM 30 m telescope and at higher resolution eight of the cores with CARMA, using CS(2-1) as the main tracer. The single-dish moment zero maps of the starless cores show single structures with central column densities ranging from 7 to 42 Multiplication-Sign 10{sup 23} cm{sup -2} and LTE masses from 20 M{sub Sun} to 154 M{sub Sun }. However, at the higher CARMA resolution (5''), all of the cores except one fragment into 3-5 components. The number of fragments is small compared to that found in some turbulent fragmentation models, although inclusion of magnetic fields may reduce the predicted fragment number and improve the model agreement. This result demonstrates that fragmentation from parsec-scale molecular clouds to sub-parsec cores continues to take place inside the starless cores. The starless cores and their fragments are embedded in larger filamentary structures, which likely played a role in the core formation and fragmentation. Most cores show clear velocity gradients, with magnitudes ranging from 1.7 to 14.3 km s{sup -1} pc{sup -1}. We modeled one of them in detail, and found that its spectra are best explained by a converging flow along a filament toward the core center; the gradients in other cores may be modeled similarly. We infer a mass inflow rate of {approx}2 Multiplication-Sign 10{sup -3} M{sub Sun} yr{sup -1}, which is in principle high enough to overcome radiation pressure and allow for massive star formation. However, the core contains multiple fragments, and it is unclear whether the rapid inflow would feed the growth of primarily a single massive star or a cluster of lower mass objects. We conclude that fast, supersonic converging flow along filaments play an important role in massive star and cluster formation.

79 ASTRONOMY AND ASTROPHYSICS↗

A SYSTEMATIC RETRIEVAL ANALYSIS OF SECONDARY ECLIPSE SPECTRA. I. A COMPARISON OF ATMOSPHERIC RETRIEVAL TECHNIQUES

Exoplanet atmosphere spectroscopy enables us to improve our understanding of exoplanets just as remote sensing in our own solar system has increased our understanding of the solar system bodies. The challenge is to quantitatively determine the range of temperatures and molecular abundances allowed by the data, which is often difficult given the low information content of most exoplanet spectra that commonly leads to degeneracies in the interpretation. A variety of spectral retrieval approaches have been applied to exoplanet spectra, but no previous investigations have sought to compare these approaches. We compare three different retrieval methods: optimal estimation, differential evolution Markov chain Monte Carlo, and bootstrap Monte Carlo on a synthetic water-dominated hot Jupiter. We discuss expectations of uncertainties in abundances and temperatures given current and potential future observations. In general, we find that the three approaches agree for high spectral resolution, high signal-to-noise data expected to come from potential future spaceborne missions, but disagree for low-resolution, low signal-to-noise spectra representative of current observations. We also compare the results from a parameterized temperature profile versus a full classical Level-by-Level approach and discriminate in which situations each of these approaches is applicable. Furthermore, we discuss the implications of our models for the inferred C-to-O ratios of exoplanetary atmospheres. Specifically, we show that in the observational limit of a few photometric points, the retrieved C/O is biased toward values near solar and near one simply due to the assumption of uninformative priors.

79 ASTRONOMY AND ASTROPHYSICS↗

Can local white dwarfs constrain dark matter interactions?

We investigate whether nearby white dwarfs (WDs) can constrain dark matter (DM) interactions with ordinary matter. As experimental sensitivity improves, driven by the Gaia mission, the sample volume of nearby WDs has been increasing over recent years. We carefully select a sample of ten cold, isolated, non-magnetic WDs within 13 pc of the Sun. We model their carbon-oxygen interior using a finite temperature relativistic equation of state and model atmospheres to infer their core temperatures. This enables us to perform a thorough estimation of the DM capture rate and evaporation mass using actual astrophysical observations. Given the low local DM density, we focus on DM that annihilates into long-lived mediators, which escape the WD and later decay into photons. While Fermi-LAT data shows no significant gamma-ray excess, future telescopes, CTA North & South, LHAASO, SWGO, could probe DM-nucleon cross sections down to ∼ 10 -41 cm 2 for DM masses above the TeV scale. Our results are competitive with current direct detection bounds (e.g., LZ) in the multi-TeV regime. This work underscores the importance of systematic WD studies in the broader landscape of DM detection and demonstrates the synergy between astrophysical and terrestrial searches in exploring DM interactions.

79 ASTRONOMY AND ASTROPHYSICS↗

Graph convolutional networks applied to unstructured flow field data

Abstract Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for an unstructured dataset. Therefore, a graph based data-driven model to perform inference on fields defined on an unstructured mesh, using a graph convolutional neural network (GCNN) is presented. The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements. The network can infer from field samples at different resolutions, and is invariant to the order in which the measurements within each sample are presented. The GCNN method, using inductive convolutional layers and adaptive pooling, is able to predict this quantity with a validation R 2 above 0.98, and a Normalized Mean Squared Error below 0.01, without relying on spatial structure.

Ogoke, Francis (ORCID:0000000224327783)↗