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DOE OSTI2016

Adaptive Sequential Monte Carlo for Multiple Changepoint Analysis

Process monitoring and control requires detection of structural changes in a data stream in real time. This paper introduces an efficient sequential Monte Carlo algorithm designed for learning unknown changepoints in continuous time. The method is intuitively simple: new changepoints for the latest window of data are proposed by conditioning only on data observed since the most recent estimated changepoint, as these observations carry most of the information about the current state of the process. The proposed method shows improved performance over the current state of the art. Another advantage of the proposed algorithm is that it can be made adaptive, varying the number of particles according to the apparent local complexity of the target changepoint probability distribution. This saves valuable computing time when changes in the changepoint distribution are negligible, and enables re-balancing of the importance weights of existing particles when a significant change in the target distribution is encountered. The plain and adaptive versions of the method are illustrated using the canonical continuous time changepoint problem of inferring the intensity of an inhomogeneous Poisson process, although the method is generally applicable to any changepoint problem. Performance is demonstrated using both conjugate and non-conjugate Bayesian models for the intensity. Lastly, appendices to the article are available online, illustrating the method on other models and applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Simulation and Optimizing Groundwater Investigations, Conceptual Design Analysis, Model Calibration and Remedial Design Using Parallel Numerical Methods: Theory and Application - 16578

Optimization of environmental restoration efforts at complex groundwater sites involves the development of a conceptual and often numerical flow and transport models. Typically, a limited number of simulations are done to assess the impact of parameter uncertainty, to evaluate remedial alternatives and develop a remedy, or to modify an existing remedy. A limited number of simulations are performed because each simulation can take upwards of several hours per simulation and until now the ability to conduct the 100's to 1000's of simulations needed to derive an optimal remedy was beyond the reach of the everyday practitioner. HGL has developed and implemented new parallel simulation and optimization methods in their software and has successfully deployed applications on high performance Cloud-based computing platforms. This advancement removes the impediment of burdensome and unpredictable runtime requirements these types of projects have typically exhibited using heuristic trial-and-error or even automated serial computation approaches. A parallel numerical solver has been added to the subsurface flow and transport simulator MODFLOW-SURFACT{sup TM} and has demonstrated reductions in solution time by as much as a factor of 9.8. This is accomplished by distributing the numerical computations of every time step over multiple processors. The integrated parallel simulation and optimization method uses the breadth and depth search approach; iteratively producing sets of tens or 100's of candidate solutions that are evaluated concurrently instead sequentially (i.e. one model evaluation at a time). Solutions within 80-100% of the globally optimal solution are regularly found expending only 5-10% of the usual computational effort using this new parallel optimization method (reducing computation effort by 10 to 20 times). Distributing optimal search runs across multiple processors additionally reduces calendar time to solutions. Practitioners evaluating multiple conceptual site models, model calibration by auto-calibration methods and remedial design by heuristics can enjoy significant reductions in model solution time by using the parallel numerical matrix solver in the physics-based simulator. Combining investigation and design analysis with breadth and depth parallel optimization methods, projects at the fringe of practical optimization, along with projects that were beyond the reach of formal optimization methods are now wholly feasible and tractable optimization problems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Approximate Boltzmann distributions in quantum approximate optimization

Approaches to compute or estimate the output probability distributions from the quantum approximate optimization algorithm (QAOA) are needed to assess the likelihood it will obtain a quantum computational advantage. We analyze output from QAOA circuits solving 7200 random MaxCut instances, with $n$ = 14–23 qubits and depth parameter $p$ ≤ 12 and find that the average basis state probabilities follow approximate Boltzmann distributions: The average probabilities scale exponentially with their energy (cut value), with a peak at the optimal solution. Furthermore, we describe the rate of exponential scaling or effective temperature in terms of a series with a leading-order term $T$ ~ $C$ min /$n$ $\sqrt{p}$, with $C$ min the optimal solution energy. Using this scaling, we generate approximate output distributions with up to 38 qubits and find these give accurate accounts of important performance metrics in cases we can simulate exactly.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2011

Resilient data staging through MxN distributed transactions.

Scientific computing-driven discoveries are frequently driven from workflows that use persistent storage as a staging area for data between operations. With the bad and progressively worse bandwidth vs. data size issues as we continue towards exascale, eliminating persistent storage through techniques like data staging will both enable these workflows to continue online, but also enable more interactive workflows reducing the time to scientific discoveries. Data staging has shown to be an effective way for applications running on high-end computing platforms to offload expensive I/O operations and to manage the tremendous amounts of data they produce. This data staging approach, however, lacks the ACID style guarantees traditional straight-to-disk methods provide. Distributed transactions are a proven way to add ACID properties to data movements, however distributed transactions follow 1xN data movement semantics, where our highly parallel HPC environments employ MxN data movement semantics. In this paper we present a novel protocol that extends distributed transaction terminology to include MxN semantics which allows our data staging areas to benefit from ACID properties. We show that with our protocol we can provide resilient data staging with a limited performance penalty over current data staging implementations.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Spoofing Cross-Entropy Measure in Boson Sampling

Cross-entropy (XE) measure is a widely used benchmark to demonstrate quantum computational advantage from sampling problems, such as random circuit sampling using superconducting qubits and boson sampling (BS). We present a heuristic classical algorithm that attains a better XE than the current BS experiments in a verifiable regime and is likely to attain a better XE score than the near-future BS experiments in a reasonable running time. The key idea behind the algorithm is that there exist distributions that correlate with the ideal BS probability distribution and that can be efficiently computed. The correlation and the computability of the distribution enable us to postselect heavy outcomes of the ideal probability distribution without computing the ideal probability, which essentially leads to a large XE. Our method scores a better XE than the recent Gaussian BS experiments when implemented at intermediate, verifiable system sizes. Much like current state-of-the-art experiments, we cannot verify that our spoofer works for quantum-advantage-size systems. However, we demonstrate that our approach works for much larger system sizes in fermion sampling, where we can efficiently compute output probabilities. Finally, we provide analytic evidence that the classical algorithm is likely to spoof noisy BS efficiently.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

Electromagnetic Simulation of the Near-Field Distribution around a Wind Farm

An efficient approach to compute the near-field distribution around and within a wind farm under plane wave excitation is proposed. To make the problem computationally tractable, several simplifying assumptions are made based on the geometry problem. By comparing the approximations against full-wave simulations at 500 MHz, it is shown that the assumptions do not introduce significant errors into the resulting near-field distribution. The near fields around a 3 × 3 wind farm are computed using the developed methodology at 150 MHz, 500 MHz, and 3 GHz. Both the multipath interference patterns and the forward shadows are predicted by the proposed method.

Yang, Shang-Te↗

DOE OSTI2016

An approach for drag correction based on the local heterogeneity for gas-solid flows

The drag models typically used for gas-solids interaction are mainly developed based on homogeneous systems of flow passing fixed particle assembly. It has been shown that the heterogeneous structures, i.e., clusters and bubbles in fluidized beds, need to be resolved to account for their effect in the numerical simulations. Since the heterogeneity is essentially captured through the local concentration gradient in the computational cells, this study proposes a simple approach to account for the non-uniformity of solids spatial distribution inside a computational cell and its effect on the interaction between gas and solid phases. Finally, to validate this approach, the predicted drag coefficient has been compared to the results from direct numerical simulations. In addition, the need to account for this type of heterogeneity is discussed for a periodic riser flow simulation with highly resolved numerical grids and the impact of the proposed correction for drag is demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Manual for the Jet Event and Background Simulation Library(JEBSimLib)

Jets are the collimated streams of particles resulting from hard scattering in the initial state of high-energy collisions. In heavy-ion collisions, jets interact with the quark-gluon plasma (QGP) before freezeout, providing a probe into the internal structure and properties of the QGP. In order to study jets, background must be subtracted from the measured event, potentially introducing a bias. We aim to understand and quantify this subtraction bias. PYTHIA, a library to simulate pure jet events, is used to simulate a model for a signature with one pure jet (a photon) and one quenched jet, where all quenched particle momenta are reduced by a user-de ned constant fraction. Background for the event is simulated using multiplicity values generated by the TRENTO initial state model of heavy-ion collisions fed into a thermal model consisting of a 3-dimensional Boltzmann distribution for particle types and momenta. Data from the simulated events is used to train a statistical model, which computes a posterior distribution of the quench factor for a data set. The model was tested rst on pure jet events and then on full events including the background. This model will allow for a quantitative determination of biases induced by various methods of background subtraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Efficient Optimization of Large Wind Farms for Real-Time Control: Preprint

Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Properly coordinating turbines, by operating some turbines suboptimally, within a wind farm has the potential to improve overall wind farm performance. Computing the optimal control strategy under varying atmospheric conditions can be computationally intense for large wind farms. As wind power farms increase in size and related models become more complex, computationally efficient algorithms are needed to perform real-time optimization and control. This study proposes a distributed optimization framework and computationally efficient wake steering wind farm control strategy that uses the yaw angle of a turbine to alter the behavior of a turbine wake and minimize turbine interactions. This computational efficiency allows the strategy to be feasible for real-time control.

17 WIND ENERGY↗

DOE OSTI2019

SPT-3G Computing

SPT-3G, the third generation camera on the South Pole Telescope (SPT), was deployed in the 2016-2017 Austral summer season. The SPT is a 10-meter telescope located at the geographic South Pole and designed for observations in the millimeter-wave and submillimeter-wave regions of the electromagnetic spectrum. The SPT is primarily used to study the cosmic microwave background (CMB). The upgraded camera produces an order of magnitude more data than the previous generations of SPT cameras. The telescope is expected to collect a petabyte (PB) of data over course of five years, which is a significantly larger data volume than any other CMB telescope in operation. The increase in data rate required radical changes to the SPT computing model both at the South Pole and University of Chicago. This paper will describe the overall integration of distributed storage and compute resources into a common interface, deployment of on-site data reduction and storage infrastructure, and the usage of the Open Science Grid (OSG) by the SPT collaboration.

Riedel, Benedikt↗

DOE OSTI2024

Alkali hydroxide (LiOH, NaOH, KOH) in water: Structural and vibrational properties, including neutron scattering results

Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+

Chemistry↗

DOE OSTI2017

Manual for the Jet Event and Background Simulation Library

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Constructing a Testing Application for GWMS

Many of Fermilab’s High Energy Physics experiments require High Throughput Computing to carry out simulations, data reconstruction, and data analysis. Glidein Workflow Management System (GWMS) is a tool that distributes High Throughput Computing. The purpose of GWMS is to provide convenient access to Grid resources and sites. GWMS is coupled to HTCondor. HTCondor is the workload management system that is used for scheduling and job control. Users submit jobs to a local HTCondor queue and GWMS will make sure the job will run on one of the many remote resources.

Neely-Brown, LeRayah↗

DOE OSTI2020

Bremsstrahlung in GRMHD Models of Accreting Black Holes

Here, the role of bremsstrahlung in the emission from hot accretion flows around slowly accreting supermassive black holes is not thoroughly understood. In order to appraise the importance of bremsstrahlung relative to other radiative processes, we compute spectral energy distributions (SEDs) of accretion disks around slowly accreting supermassive black holes including synchrotron radiation, inverse Compton scattering, and bremsstrahlung. We compute SEDs for (i) four axisymmetric radiative general relativistic magnetohydrodynamics (RadGRMHD) simulations of 10 8 M ⊙ black holes with accretion rates between 10 -8 $\dot{M}$ Edd and 10 -5 $\dot{M}$ Edd , (ii) four axisymmetric RadGRMHD simulations of M87* with varying dimensionless spin a$_*$ and black hole mass, and (iii) a 3D GRMHD simulation scaled for Sgr A*. At 10 -8 $\dot{M}$ Edd , most of the luminosity is synchrotron radiation, while at 10 -5 $\dot{M}$ Edd the three radiative processes have similar luminosities. In most models, bremsstrahlung dominates the SED near 512 keV. In the M87* models, bremsstrahlung dominates this part of the SED if a$_*$ = 0.5, but inverse Compton scattering dominates if a$_*$ = 0.9375. Since scattering is more variable than bremsstrahlung, this result suggests that 512 keV variability could be a diagnostic of black hole spin. In the appendix, we compare some bremsstrahlung formulæ found in the literature.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

Light-Front Holography, Light-Front Wavefunctions, and Novel QCD Phenomena

Light-Front Holography is one of the most remarkable features of the AdS/CFT correspondence. In spite of its present limitations it provides important physical insights into the nonperturbative regime of QCD and its transition to the perturbative domain. This novel framework allows hadronic amplitudes in a higher dimensional anti-de Sitter (AdS) space to be mapped to frame-independent light-front wavefunctions of hadrons in physical space-time. The model leads to an effective confining light-front QCD Hamiltonian and a single-variable light-front Schroedinger equation which determines the eigenspectrum and the light-front wavefunctions of hadrons for general spin and orbital angular momentum. The coordinate z in AdS space is uniquely identified with a Lorentz-invariant coordinate {zeta} which measures the separation of the constituents within a hadron at equal light-front time and determines the off-shell dynamics of the bound-state wavefunctions, and thus the fall-off as a function of the invariant mass of the constituents. The soft-wall holographic model modified by a positive-sign dilaton metric, leads to a remarkable one-parameter description of nonperturbative hadron dynamics - a semi-classical frame-independent first approximation to the spectra and light-front wavefunctions of meson and baryons. The model predicts a Regge spectrum of linear trajectories with the same slope in the leading orbital angular momentum L of hadrons and the radial quantum number n. The hadron eigensolutions projected on the free Fock basis provides the complete set of valence and non-valence light-front Fock state wavefunctions {Psi}{sub n/H} (x{sub i}, k{sub {perpendicular}i}, {lambda}{sub i}) which describe the hadron's momentum and spin distributions needed to compute the direct measures of hadron structure at the quark and gluon level, such as elastic and transition form factors, distribution amplitudes, structure functions, generalized parton distributions and transverse momentum distributions. The effective confining potential also creates quark-antiquark pairs from the amplitude q {yields} q{bar q}q. Thus in holographic QCD higher Fock states can have any number of extra q{bar q} pairs. We discuss the relevance of higher Fock-states for describing the detailed structure of space and time-like form factors. The AdS/QCD model can be systematically improved by using its complete orthonormal solutions to diagonalize the full QCD light-front Hamiltonian or by applying the Lippmann-Schwinger method in order to systematically include the QCD interaction terms. A new perspective on quark and gluon condensates is also obtained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2012

Absolute nuclear material assay

A method of absolute nuclear material assay of an unknown source comprising counting neutrons from the unknown source and providing an absolute nuclear material assay utilizing a model to optimally compare to the measured count distributions. In one embodiment, the step of providing an absolute nuclear material assay comprises utilizing a random sampling of analytically computed fission chain distributions to generate a continuous time-evolving sequence of event-counts by spreading the fission chain distribution in time.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2012

Absolute nuclear material assay using count distribution (LAMBDA) space

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗