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DOE OSTI2019

The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: a tomographic measurement of structure growth and expansion rate from anisotropic galaxy clustering in Fourier space

In this study, we perform a tomographic structure growth and expansion rate analysis using the monopole, quadrupole and hexadecapole of the redshift-space galaxy power spectrum derived from the Sloan Digital Sky Survey (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12 combined sample, which covers the redshift range of 0.20 < $z$ < 0.75. By allowing for overlap between neighbouring redshift slices in order to extract information on the light-cone, we successfully obtain joint BAO and RSD constraints with a precision of 2-3 per cent for D A , 3-10 per cent for H and 9-12 per cent for fσ 8 with a redshift resolution of $Δz$ ~ 0.04. Our measurement is consistent with that presented in Wang et al., where the analysis is performed in configuration space. Finally, we apply our measurement to constrain the Em>f(R) gravity model, and find that the 95 per cent CL upper limit of log 10 B 0 can be reduced by 11 per cent by our tomographic BAO and RSD measurements.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Generative Thermodynamic Computing

Here, we introduce a generative modeling framework for thermodynamic computing, in which structured data are synthesized from noise by the natural time evolution of a physical system governed by Langevin dynamics. While conventional diffusion models use neural networks to perform denoising, here the information needed to generate structure from noise is encoded by the dynamics of a thermodynamic system. Training proceeds by maximizing the probability with which the computer generates the reverse of a noising trajectory, which ensures that the computer generates data with minimal heat emission. We demonstrate this framework within a digital simulation of a thermodynamic computer. If realized in analog hardware, such a system would function as a generative model that produces structured samples without the need for artificially injected noise or active control of denoising.

Whitelam, Stephen [Lawrence Berkeley National Labo↗

DOE OSTI2022

Estimating Channel Width for the Middle Green River Using Remote Sensing

Channel geometry can affect the abundance and quality of fish habitats along rivers in the Colorado River basin. However, previous analyses of sediment transport and channel narrowing in the middle Green River have focused on very limited reaches of the river and have used techniques with varying degrees of resolution, leading to potential problems with interpretation and generalization to the overall reach. The goal of this study is to develop comprehensive baseline information for channel width estimation along the Green River by combining a high-resolution digital elevation model (DEM) derived from Light Detection and Ranging (LiDAR) data and Sentinel-2 multispectral satellite imagery. The objectives are to (1) estimate channel width using Sentinel-2 satellite imagery alone, (2) estimate channel width using the high-resolution DEM with ancillary information extracted from the multispectral imagery, and (3) examine the agreement in channel width estimates between the products derived from the two methods to gain insights for cost effective channel-width monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

The Color and Stellar Mass Dependence of Small-scale Galaxy Clustering in SDSS-III BOSS

We measure the color and stellar mass dependence of clustering in spectroscopic galaxies at 0.6 < z < 0.65 using data from the Baryon Oscillation Spectroscopic Survey component of the Sloan Digital Sky Survey. We greatly increase the statistical precision of our clustering measurements by using the cross-correlation of 66,657 spectroscopic galaxies to a sample of 6.6 million fainter photometric galaxies. The clustering amplitude w(R) is measured as the ratio of the mean excess number of photometric galaxies found within a specified radius annulus around a spectroscopic galaxy to that from a random photometric galaxy distribution. We recover many of the familiar trends at high signal-to-noise ratio. We find the ratio of the clustering amplitudes of red and blue massive galaxies to be w{sub red}/w{sub blue}=1.92±0.11 in our smallest annulus of 75–125 kpc. At our largest radii (2–4 Mpc), we find w{sub red}/w{sub blue}=1.24±0.05. Red galaxies therefore have denser environments than their blue counterparts at z ∼ 0.625, and this effect increases with decreasing radius. Irrespective of color, we find that w(R) does not obey a simple power-law relation with radius, showing a dip around 1 Mpc. Holding stellar mass fixed, we find a clear differentiation between clustering in red and blue galaxies, showing that clustering is not solely determined by stellar mass. Holding color fixed, we find that clustering increases with stellar mass, especially for red galaxies at small scales (more than a factor of 2 effect over 0.75 dex in stellar mass).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Measurements of Effective Optical Depth in the Lyα Forest from the BOSS DR12 Quasar Sample

We measure the effective optical depth in the Lyα forest using 40,035 quasar spectra from the Twelfth Data Release (DR12) of the Baryon Oscillation Spectroscopic Survey of Sloan Digital Sky Survey IV. A rigorous selection based on spectral index and the equivalent width of the C iv emission line is applied to choose seven uniform samples with minimal intrinsic variations across redshifts. Modeling the redshift evolution of the effective optical depth with a power law, ${\tau }_{\mathrm{eff}}={\tau }_{0}{(1+z)}^{\gamma },$ produces ${\tau }_{0}=(5.54\pm 0.64)\times {10}^{-3}$ and $\gamma =3.182\pm 0.074$. The 2.2% precision estimate on γ is dominated by systematic errors, likely arising from the bias and uncertainties in spectral index estimates. Even after incorporating the systematic errors, this work provides the most precise estimates of optical depth parameters to date. Finally, using the reconstructed Lyα forest continuum to directly measure the transmitted flux ratio as a function of redshift, we find deviations of less than 2.5% from the predictions from the global model and no convincing evidence for signal associated with He ii reionization.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Evidence for Impact of Galaxy Mergers on Stellar Kinematics of Early-type Galaxies

We provide observational evidence that galaxy mergers significantly affect stellar kinematics of early-type galaxies (ETGs) such as specific stellar angular momentum within the half-light radius (${\lambda }_{{R}_{e}}$) and kinematic misalignment (ψ mis ), using MaNGA integral field unit spectroscopic data that are in the Stripe 82 region of the Sloan Digital Sky Survey. In this study, tidal features around ETGs, which are detected in deep coadded images, are used as direct evidence for mergers that occurred recently. In the case of ETGs that do not have dust lanes, ${\lambda }_{{R}_{e}}$ is lower in ETGs with tidal features than in those without tidal features (median ${\lambda }_{{R}_{e}}$: 0.21 versus 0.39) in all stellar mass and Sérsic index ranges except the most massive bin, so that the fraction of ETGs with tidal features in slow rotators is more than twice as large as that in fast rotators (42% versus 18%). Moreover, ETGs with tidal features have larger ψ mis than those without tidal features (mean ψ mis : 28° versus 15°). By contrast, ETGs with dust lanes are fast rotators, and ETGs with both dust lanes and tidal features have the highest ${\lambda }_{{R}_{e}}$ (median ${\lambda }_{{R}_{e}}$: 0.59) among all ETG categories. In addition, ETGs with dust lanes have small ψ mis regardless of the existence of tidal features (Ψ mis <7$^{°}_{.}$5). Our results can be explained if mergers with different gas fractions generate merger remnants that have different kinematic properties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Condition monitoring of wind turbine drivetrains: state-of-the-art technologies, recent trends, and future outlook

As global wind capacity expands, reducing operations and maintenance costs is critical to lowering the levelized cost of energy. This paper explores the state of the art in condition monitoring and prognostic strategies for wind turbine drivetrains, which are among the most failure-prone and maintenance-intensive subsystems. Current diagnostic methodologies are evaluated, covering supervisory control and data acquisition (SCADA) data, high-frequency vibration and acoustic analysis, machine learning and digital twin frameworks. Finally, practical challenges are identified that limit wide-scale industrial adoption, in order to guide future research and industrial efforts.

17 WIND ENERGY↗

DOE OSTI2015

Design and Testing of a Liquid Nitrous Oxide and Ethanol Fueled Rocket Engine

A small-scale, bi-propellant, liquid fueled rocket engine and supporting test infrastructure were designed and constructed at the Energetic Materials Research and Testing Center (EMRTC). This facility was used to evaluate liquid nitrous oxide and ethanol as potential rocket propellants. Thrust and pressure measurements along with high-speed digital imaging of the rocket exhaust plume were made. This experimental data was used for validation of a computational model developed of the rocket engine tested. The developed computational model was utilized to analyze rocket engine performance across a range of operating pressures, fuel-oxidizer mixture ratios, and outlet nozzle configurations. A comparative study of the modeling of a liquid rocket engine was performed using NASA CEA and Cantera, an opensource equilibrium code capable of being interfaced with MATLAB. One goal of this modeling was to demonstrate the ability of Cantera to accurately model the basic chemical equilibrium, thermodynamics, and transport properties for varied fuel and oxidizer operating conditions. Once validated for basic equilibrium, an expanded MATLAB code, referencing Cantera, was advanced beyond CEAs capabilities to predict rocket engine performance as a function of supplied propellant flow rate and rocket engine nozzle dimensions. Cantera was found to comparable favorably to CEA for making equilibrium calculations, supporting its use as an alternative to CEA. The developed rocket engine performs as predicted, demonstrating the developedMATLAB rocket engine model was successful in predicting real world rocket engine performance. Finally, nitrous oxide and ethanol were shown to perform well as rocket propellants, with specific impulses experimentally recorded in the range of 250 to 260 seconds.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Active cryogenic electronic envelope

A data acquisition module is disclosed for use with electronic components such as superconductive magnets wherein the electronic components and the data acquisition module may reside inside a cryostat. The data acquisition module allows conventional electronic technologies to work inside cryostats at cryogenic temperatures as low as 4° K. This is achieved by management of heat inside the module to keep the module operating at a temperature above the cryogenic temperature. This approach avoids the difficulties that arise from changes in carrier mobility in semiconductors operating at deep cryogenic temperatures. A cryogenic system comprises a cryostat containing a cryogenic fluid and at least one sensor, and a data acquisition module located within the cryostat, the module in communication with the at least one sensor to acquire data, wherein the module comprises a thermal package, a conditioning and digitizing electronic module, a controller module, a power module, and a thermal management module.

42 ENGINEERING↗

DOE OSTI2013

THE MOST METAL-POOR STARS. I. DISCOVERY, DATA, AND ATMOSPHERIC PARAMETERS

We report the discovery of 34 stars in the Hamburg/ESO Survey for metal-poor stars and the Sloan Digital Sky Survey that have [Fe/H] {approx}< -3.0. Their median and minimum abundances are [Fe/H] = -3.1 and -4.1, respectively, while 10 stars have [Fe/H] < -3.5. High-resolution, high signal-to-noise spectroscopic data-equivalent widths and radial velocities-are presented for these stars, together with an additional four objects previously reported or currently being investigated elsewhere. We have determined the atmospheric parameters, effective temperature (T {sub eff}), and surface gravity (log g), which are critical in the determination of the chemical abundances and the evolutionary status of these stars. Three techniques were used to derive these parameters. Spectrophotometric fits to model atmosphere fluxes were used to derive T {sub eff}, log g, and an estimate of E(B - V); H{alpha}, H{beta}, and H{gamma} profile fitting to model atmosphere results provided the second determination of T {sub eff} and log g; and finally, we used an empirical T {sub eff}-calibrated H{delta} index, for the third, independent T {sub eff} determination. The three values of T {sub eff} are in good agreement, although the profile fitting may yield systematically cooler T {sub eff} values, by {approx}100 K. This collective data set will be analyzed in future papers in the present series to utilize the most metal-poor stars as probes of conditions in the early universe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Standardized Architecture for a Mega-Biobank Phenomic Library: The Million Veteran Program (MVP)

Electronic health records (EHRs) provide a wealth of data for phenotype development in population health studies, and researchers invest considerable time to curate data elements and validate disease definitions. The ability to reproduce well-defined phenotypes increases data quality, comparability of results and expedites research. In this paper, we present a standardized approach to organize and capture phenotype definitions, resulting in the creation of an open, online repository of phenotypes. This resource captures phenotype development, provenance and process from the Million Veteran Program, a national mega-biobank embedded in the Veterans Health Administration (VHA). To ensure that the repository is searchable, extendable, and sustainable, it is necessary to develop both a proper digital catalog architecture and underlying metadata infrastructure to enable effective management of the data fields required to define each phenotype. Our methods provide a resource for VHA investigators and a roadmap for researchers interested in standardizing their phenotype definitions to increase portability.

Knight, Kathryn↗

DOE OSTI2020

Standardized Architecture for a Mega-Biobank Phenomic Library: The Million Veteran Program (MVP)

Knight, Kathryn↗

DOE OSTI2020

Electronic Bottleneck Suppression in Next‐Generation Networks with Integrated Photonic Digital‐to‐Analog Converters

Digital‐to‐analog converters (DAC) are indispensable functional units in signal processing instrumentation and wide‐band telecommunication links for both civil and military applications. As photonic systems are capable of high data throughput and low latency, an increasingly found system limitation stems from the required domain crossing such as digital to analog and electronic to optical. A photonic DAC implementation, in contrast, enables a seamless signal conversion with respect to both energy efficiency and short signal delay, often requiring bulky discrete optical components and electric–optic transformation, hence introducing inefficiencies. Herein, a novel coherent parallel photonic DAC concept along with a 4‐bit experimental prototype capable of performing this DAC without optic–electric–optic domain crossing is introduced. This new paradigm guarantees a linear intensity weighting among bits when operating at high sampling rates (50 GHz), featuring an exceptional sampling efficiency (> 100 GS ) and small footprint (≈1 mm 2 ) in an 8‐bit implementation. Importantly, this photonic DAC enables seamless interfaces of next‐generation data processing hardware with high relevance in data centers, task‐specific compute accelerators such as neuromorphic engines, and network edge processing applications.

Meng, Jiawei↗

DOE OSTI2012

Demodulator electronics for laser vibrometry

One of the most important parts of a fiber-laser vibrometer is demodulation electronic section. The distortion, nonlinearity, offset and added noise of measured signal come from electronic circuits and they have direct influence on finale measuring results. Two main parameters of an investigated vibrating object: velocity V(t) and displacement s(t), influence of detected beat signals. They are: the Doppler frequency deviation f(t) and phase shift {phi}(t), respectively. Because of wide range of deviations it is difficult to use just one demodulator. That is the reason why we use three different types of demodulators. The first one is the IQ demodulator, which is the most sensitive one and its output is proportional to the displacement. Each IQ channel is sampled simultaneously by an analog to digital converter (ADC) integrated in a digital signal processor (DSP). The output signals from the two FM demodulators are proportional to the frequency deviation of heterodyne signals. They are sensitive directly to the velocity of the object. The main disadvantage of scattered light interferometry system is a ''speckle effect'', appearing in relatively large amplitude fluctuation of a heterodyne signal. To minimize ''speckle effect'' influence on quality of beat signals we applied the automatic gain control (AGC) system. Data acquisition, further signal processing (e.g. vibration frequency spectra) and presentation of results is realized by PC via USB interface.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2012

Assembling large, complex environmental metagenomes

The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two pre-assembly filtering approaches, digital normalization and partitioning, to make large metagenome assemblies more computationaly tractable. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.

Astronomy & Astrophysics↗

DOE OSTI2021

Experimental Images and Videos of Foam Stability (Half-life)

The experimental data obtained in this project is the thermal stability data of various foams measured using the setup established at Temple University during this study. The setup is installed with a portable digital camera which can take images and videos of foam evolution at a given pressure and temperature condition. Consequently, the half-life data was recorded from the images/videos, which are used as a measure of the thermal stability for foams. Over the 3 years of this project, four different surfactants and five different stabilizing agents were studied. The surfactants are, Alfa Olefin Sulfonate (AOS), Sodium Dodecyl Sulphate (SDS), Tergitol (NP-40), and Cetyltrimethylammonium chloride (CTAC). The stabilizing agents are, guar gum, bentonite clay, crosslinking agents, silicon dioxide nanoparticles (60 to 70nm), and graphene oxide dispersions. Foam stability was evaluated at different temperatures between 100C and 200cC, while the foam generation pressure varied between atmospheric pressure (14.7 psi) and 1000 psi. The images are saved as .jpg file and videos are saved as .avi files.

15 GEOTHERMAL ENERGY↗