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At least 343 records · Page 19

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DOE OSTI2019

Topological data analysis for the string landscape

Persistent homology computes the multiscale topology of a data set by using a sequence of discrete complexes. In this paper, we propose that persistent homology may be a useful tool for studying the structure of the landscape of string vacua. As a scaled-down version of the program, we use persistent homology to characterize distributions of Type IIB flux vacua on moduli space for three examples: the rigid Calabi-Yau, a hypersurface in weighted projective space, and the symmetric six-torus $T^6 = (T^2)^3$. These examples suggest that persistence pairing and multiparameter persistence contain useful information for characterization of the landscape in addition to the usual information contained in standard persistent homology. We also study how restricting to special vacua with phenomenologically interesting low-energy properties affects the topology of a distribution

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results

Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

Photon Counting Data Analysis: Application of the Maximum Likelihood and Related Methods for the Determination of Lifetimes in Mixtures of Rose Bengal and Rhodamine B

It is often convenient to know the minimum amount of data needed in order to obtain a result of desired accuracy and precision. It is a necessity in the case of subdiffraction-limited microscopies, such as stimulated emission depletion (STED) microscopy, owing to the limited sample volumes and the extreme sensitivity of the samples to photobleaching and photodamage. We present a detailed comparison of probability-based techniques (the maximum likelihood method and methods based on the binomial and the Poisson distributions) with residual minimization-based techniques for retrieving the fluorescence decay parameters for various two-fluorophore mixtures, as a function of the total number of photon counts, in time-correlated, single-photon counting experiments. The probability-based techniques proved to be the most robust (insensitive to initial values) in retrieving the target parameters and, in fact, performed equivalently to 2-3 significant figures. This is to be expected, as we demonstrate that the three methods are fundamentally related. Furthermore, methods based on the Poisson and binomial distributions have the desirable feature of providing a bin-by-bin analysis of a single fluorescence decay trace, which thus permits statistics to be acquired using only the one trace for not only the mean and median values of the fluorescence decay parameters but also for the associated standard deviations. Lastly, these probability-based methods lend themselves well to the analysis of the sparse data sets that are encountered in subdiffraction-limited microscopies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

GRDC. A Collaborative Framework for Radiological Background and Contextual Data Analysis

The Radiation Mobile Analysis Platform (RadMAP) is unique in its capability to collect both high quality radiological data from both gamma-ray detectors and fast neutron detectors and a broad array of contextual data that includes positioning and stance data, high-resolution 3D radiological data from weather sensors, LiDAR, and visual and hyperspectral cameras. The datasets obtained from RadMAP are both voluminous and complex and require analyses from highly diverse communities within both the national laboratory and academic communities. Maintaining a high level of transparency will enable analysis products to further enrich the RadMAP dataset. It is in this spirit of open and collaborative data that the RadMAP team proposed to collect, calibrate, and make available online data from the RadMAP system. The Berkeley Data Cloud (BDC) is a cloud-based data management framework that enables web-based data browsing visualization, and connects curated datasets to custom workflows such that analysis products can be managed and disseminated while maintaining user access rights. BDC enables cloud-based analyses of large datasets in a manner that simulates real-time data collection, such that BDC can be used to test algorithm performance on real and source-injected datasets. Using the BDC framework, a subset of the RadMAP datasets have been disseminated via the Gamma Ray Data Cloud (GRDC) that is hosted through the National Energy Research Science Computing (NERSC) Center, enabling data access to over 40 users at 10 institutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

FAST Drift Scan Survey for Ηι Intensity Mapping: I. Preliminary Data Analysis

This work presents the initial results of the drift-scan observation for the neutral hydrogen (Hi) intensity mapping survey with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The data analyzed in this work were collected in night observations from 2019 through 2021. The primary findings are based on 28 hr of drift-scan observation carried out over 7 nights in 2021, which covers 60 deg 2 sky area. Our main findings are, first, our calibration strategy can successfully correct both the temporal and bandpass gain variation over the 4 hr drift-scan observation. Second, the continuum maps of the surveyed region are made with frequency resolution of 28 kHz and pixel area of $_{2.95\,{\mathrm{arcmin}}^{2}}$. The pixel noise levels of the continuum maps are slightly higher than the forecast assuming $T$ sys = 20 K, which are 36.0 mK (for 10.0 s integration time) at the 1050–1150 MHz band, and 25.9 mK (for 16.7 s integration time) at the 1323–1450 MHz band, respectively. Third, the flux-weighted differential number count is consistent with the NRAO-VLA Sky Survey (NVSS) catalog down to the confusion limit ~7 mJy beam –1 . Finally, the continuum flux measurements of the sources are consistent with those found in the literature. The difference in the flux measurement of 81 isolated NVSS sources is about 6.3%. Our research offers a systematic analysis for the FAST Hi intensity mapping drift-scan survey and serves as a helpful resource for further cosmology and associated galaxies sciences with the FAST drift-scan survey.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Linac Coherent Light Source data analysis using psana

Psana ( Photon Science Analysis ) is a software package that is used to analyze data produced by the Linac Coherent Light Source X-ray free-electron laser at the SLAC National Accelerator Laboratory. The project began in 2011, is written primarily in C++ with some Python, and provides user interfaces in both C++ and Python. Most users use the Python interface. The same code can be run in real time while data are being taken as well as offline, executing on many nodes/cores using MPI for parallelization. It is publicly available and installable on the RHEL5/6/7 operating systems.

Damiani, D.↗

DOE OSTI2012

Experimental Evaluation of Multi-spacecraft Data Analysis Techniques in a Laboratory Plasma

The Magnetic Reconnection Experiment (MRX)[1] has been utilized to assess the effectiveness of minimum variance analysis on the magnetic field (MVAB) and boundary-crossing time analysis (BCTA). The neutral sheet is swept, or jogged, in a controlled manner with respect to the stationary probes by pulsed internal coil currents. Magnetic field data from measurement points resembling data from multi-spacecraft flying though a reconnecting current sheet is used to check both techniques to deduce a proper normal vector. We examine discharges with the two-dimensional (2-D) X-line structure as well as cases in which a flux rope forms within the layer. All discharges are in a two-fluid regime in which electrons are magnetized but not ions. Boundary-crossing time analysis with four sample measurement points forming a tetrahedron generates a reasonable unit normal vector and relative velocity along the normal vector for all of the tested cases. On the other hand, MVAB sometimes fails to predict a proper normal direction. This is because the X-line magnetic geometry is fundamentally 2-D or 3-D. However, the direction along the reconnecting field determined by MVAB does not deviate much from the real magnetic geometry documented by 2-D magnetic probe arrays and one additional probe at a different toroidal location. Based on these observations, we suggest a procedure for determining a local coordinate system for data from the Magnetospheric Multi-Scale (MMS) mission when spacecraft passes through a reconnecting current sheet. The distance between measurement points on the order of the ion skin depth ($c/\omega_{pi}$) is pertinent to determination of the magnetic geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

SMART Wind Turbine Rotor: Data Analysis and Conclusions

The Wind Energy Technologies department at Sandia National Laboratories has developed and field tested a wind turbine rotor with integrated trailing-edge flaps designed for active control of the rotor aerodynamics. The SMART Rotor project was funded by the Wind and Water Power Technologies Office of the U.S. Department of Energy (DOE) and was conducted to demonstrate active rotor control and evaluate simulation tools available for active control research. This report documents the data post-processing and analysis performed to date on the field test data. Results include the control capability of the trailing edge flaps, the combined structural and aerodynamic damping observed through application of step actuation with ensemble averaging, direct observation of time delays associated with aerodynamic response, and techniques for characterizing an operating turbine with active rotor control.

17 WIND ENERGY↗

DOE OSTI2015

Arecibo pulsar survey using ALFA. IV. Mock spectrometer data analysis, survey sensitivity, and the discovery of 40 pulsars

The on-going Arecibo Pulsar-ALFA (PALFA) survey began in 2004 and is searching for radio pulsars in the Galactic plane at 1.4 GHz. Here we present a comprehensive description of one of its main data reduction pipelines that is based on the PRESTO software and includes new interference-excision algorithms and candidate selection heuristics. This pipeline has been used to discover 40 pulsars, bringing the survey’s discovery total to 144 pulsars. Of the new discoveries, eight are millisecond pulsars (MSPs; P<10 ms) and one is a Fast Radio Burst (FRB). This pipeline has also re-detected 188 previously known pulsars, 60 of them previously discovered by the other PALFA pipelines. We present a novel method for determining the survey sensitivity that accurately takes into account the effects of interference and red noise: we inject synthetic pulsar signals with various parameters into real survey observations and then attempt to recover them with our pipeline. We find that the PALFA survey achieves the sensitivity to MSPs predicted by theoretical models but suffers a degradation for P≳100 ms that gradually becomes up to ∼10 times worse for P>4 s at DM<150 pc cm{sup −3}. We estimate 33 ± 3% of the slower pulsars are missed, largely due to red noise. A population synthesis analysis using the sensitivity limits we measured suggests the PALFA survey should have found 224 ± 16 un-recycled pulsars in the data set analyzed, in agreement with the 241 actually detected. The reduced sensitivity could have implications on estimates of the number of long-period pulsars in the Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Differentially Private K -Means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. Here, in this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Eastern North Atlantic (ENA) Aerosol Supplementary Site (S1) Data Analysis Report

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Eastern North Atlantic (ENA) Aerosol Supplementary Site (S1) Data Analysis Report

We present strategies to identify time periods when local aerosol sources impact the data collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility in the Eastern North Atlantic (ENA). Data from the aerosol observing system (AOS) at the Central Facility and a temporary aerosol Supplementary Site (S1) were evaluated. S1 instrumentation was deployed within 0.75 km of the ENA Central Facility (C1) for approximately one year. Two time periods, one during the summer and one during the winter, were examined in support of the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) intensive operating periods (IOPs) in 2017 and 2018. As the criteria for identifying regionally representative data varies based on the specific needs of the user, we present and discuss different strategies using measured aerosol properties and their associated meteorological parameters to isolate periods in AOS data that were impacted by local aerosol at ENA. We identify high-concentration aerosol events at ENA using submicron number concentrations, size distributions, high-time-resolution measurements, and diurnal profiles. An improved understanding of local aerosol to select the optimal aerosol mask was obtained with an understanding of local meteorology during the different seasons. We developed a mathematical algorithm that employs AOS data to mask high-concentration aerosol events at ENA. We validated the ENA Aerosol Mask with aerosol data from C1 and S1 and associated meteorological data. The Supplementary Site was ideal to understand and constrain local aerosol sources within the region. In its absence, collocated data from the ENA AOS could be used to select appropriate parameters to develop an aerosol mask that would limit over- or underestimating the regional baseline aerosol data. When available, associated metadata, such as airport flight logs and visual identification with AOS cameras should also be used to validate the application of an aerosol mask at a new site. Our results confirm the importance of optimizing an aerosol mask prior its application. In the future, the application of an aerosol mask at ARM observatories and during mobile deployments could be used to increase the data quality of continuous AOS data and to reduce uncertainties when parameterizing regional aerosol processes and impacts on clouds for incorporation in models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Towards Redefining the Reproducibility in Quantum Computing: A Data Analysis Approach on NISQ Devices

Although the building of quantum computers has kept making rapid progress in recent years, noise is still the main challenge for any application to leverage the power of quantum computing. Existing works addressing noise in quantum devices proposed noise reduction when deploying a quantum algorithm to a specified quantum computer. The reproducibility issue of quantum algorithms has been raised since the noise levels vary on different quantum computers. Importantly, existing works largely ignore the fact that the noise of quantum devices varies as time goes by. Therefore, reproducing the results on the same hardware will even become a problem. We analyze the reproducibility of quantum machine learning (QML) algorithms based on daily model training and execution data collection. Our analysis shows a correlation between our QML models’ test accuracy and quantum computer hardware’s calibration features. We also demonstrate that noisy simulators for quantum computers are not a reliable tool for quantum machine learning applications.

Senapati, Priyabrata↗

DOE OSTI2017

Joint small-angle X-ray and neutron scattering data analysis of asymmetric lipid vesicles

Low- and high-resolution models describing the internal transbilayer structure of asymmetric lipid vesicles have been developed. These models can be used for the joint analysis of small-angle neutron and X-ray scattering data. The models describe the underlying scattering length density/electron density profiles either in terms of slabs or through the so-called scattering density profile, previously applied to symmetric lipid vesicles. Both models yield structural details of asymmetric membranes, such as the individual area per lipid, and the hydrocarbon thickness of the inner and outer bilayer leaflets. The scattering density profile model, however, comes at a cost of increased computational effort but results in greater structural resolution, showing a slightly lower packing of lipids in the outer bilayer leaflet of ~120 nm diameter palmitoyloleoyl phosphatidylcholine (POPC) vesicles, compared to the inner leaflet. Here, analysis of asymmetric dipalmitoyl phosphatidylcholine/POPC vesicles did not reveal evidence of transbilayer coupling between the inner and outer leaflets at 323 K, i.e. above the melting transition temperature of the two lipids.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

A reconsideration of the noise equivalent power and the data analysis procedure for the infrared imaging video bolometers

The infrared imaging video bolometer (IRVB) used for measurement of the two-dimensional (2D) radiation profiles from the Large Helical Device has been significantly upgraded recently to improve its signal to noise ratio, sensitivity, and calibration, which ultimately provides quantitative measurements of the radiation from the plasma. The reliability of the quantified data needs to be established by various checks. The noise estimates also need to be revised and more realistic values need to be established. It is shown that the 2D heat diffusion equation can be used for estimating the power falling on the IRVB foil, even with a significant amount of spatial variation in the thermal diffusivity across the area of the platinum foil found experimentally during foil calibration. The equation for the noise equivalent power density (NEPD) is re-derived to include the errors in the measurement of the thermophysical and the optical properties of the IRVB foil. The theoretical value estimated using this newly derived equation matches closely, within 5.5%, with the mean experimental value. The change in the contribution of each error term of the NEPD equation with rising foil temperature is also studied and the blackbody term is found to dominate the other terms at elevated operating temperatures. The IRVB foil is also sensitive to the charge exchange (CX) neutrals escaping from the plasma. The CX neutral contribution is estimated to be marginally higher than the noise equivalent power (NEP) of the IRVB. It is also established that the radiation measured by the IRVB originates from the impurity line radiation from the plasma and not from the heated divertor tiles. The change in the power density due to noise reduction measures such as data smoothing and averaging is found to be comparable to the IRVB NEPD. The precautions that need to be considered during background subtraction are also discussed with experimental illustrations. Finally, the analysis algorithm with all the improvements is validated and found to reproduce the input power well within 10% accuracy. This article answers many fundamental questions relevant to the IRVB and illustrates the care to be exercised while processing the IRVB data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

Extracting physics through deep data analysis

In recent decades humankind has become very apt at generating and recording enormous amounts of data, ranging from tweets and selfies on social networks, to financial transactions in banks and stores. The scientific community has not shunned this popular trend and now routinely produces hundreds of petabytes of data per year [1]. This is because materials and phenomena in the world around us exist in an interweaved, entangled form, which gives rise to the complexity of the Universe and determines the size and complexity of the data that describes it. Science and technology endeavor to unravel this convolution and extract pure components from the mixtures, be it in ore mining and metal smelting or separation of thermal conductivity into the electronic and phononic contributions. Decomposition of complex behavior is the key to understanding manifestations of Nature. However, tools to carry out this task are not readily available, and therefore, intricate systems often remain well-characterized experimentally, but still not well understood due to intricacy of the collected data. Lastly, in materials science, understanding and ultimately designing new materials with complex properties will require the ability to integrate and analyze data from multiple instruments, including computational models, designed to probe complementary ranges of space, time, and energy.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Value of Information and Data Management Platform Promoting Modern Data Analysis Utilization. STTR Phase I, Final Technical Report

The immense growth in weather and river sensor data availability along with increases in computational power and algorithmic sophistication have enabled the widespread application of physical, machine learning, and artificial intelligence algorithms to river forecasting. The forecasts produced by widely varying model approaches are inherently limited in different ways, such as by the number and quality of training observations, producing biased predictions that achieve optimal skill only over specific ranges of weather and river conditions. Thus, despite the growing availability and new sources of forecasts, hydropower owners cannot fully leverage the information because they lack an effective mechanism to determine the “best” forecast for given conditions or objectives. Tools are needed to consolidate forecast information, demonstrate skill, and extract optimal forecast guidance from the range of possibilities. Such tools will increase uptake and beneficial use of the growing wealth of forecast information, and lead to improved operating decisions, optimal power generation, and enhanced system value. This research proposes to improve the value of forecasts by developing a broadly accessible verification framework and applying machine learning techniques to identify the ‘best’ forecast from an ensemble of sources given the current weather, river, and operating conditions.

13 HYDRO ENERGY↗