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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The Sloan Digital Sky Survey Quasar Catalog: Twelfth data release

In this paper, we present the Data Release 12 Quasar catalog (DR12Q) from the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III. This catalog includes all SDSS-III/BOSS objects that were spectroscopically targeted as quasar candidates during the full survey and that are confirmed as quasars via visual inspection of the spectra, have luminosities M i [z = 2] < -20.5 (in a ΛCDM cosmology with H 0 = 70 km s -1 Mpc -1 , Ω M = 0.3, and Ω Λ = 0.7), and either display at least one emission line with a full width at half maximum (FWHM) larger than 500 km s -1 or, if not, have interesting/complex absorption features. The catalog also includes previously known quasars (mostly from SDSS-I and II) that were reobserved by BOSS. The catalog contains 297 301 quasars (272 026 are new discoveries since the beginning of SDSS-III) detected over 9376 deg 2 with robust identification and redshift measured by a combination of principal component eigenspectra. The number of quasars with z > 2.15 (184 101, of which 167 742 are new discoveries) is about an order of magnitude greater than the number of z > 2.15 quasars known prior to BOSS. Redshifts and FWHMs are provided for the strongest emission lines (C iv, C iii], Mg ii). The catalog identifies 29 580 broad absorption line quasars and lists their characteristics. For each object, the catalog presents five-band (u, g, r, i, z) CCD-based photometry with typical accuracy of 0.03 mag together with some information on the optical morphology and the selection criteria. When available, the catalog also provides information on the optical variability of quasars using SDSS and Palomar Transient Factory multi-epoch photometry. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra, covering the wavelength region 3600–10 500 Å at a spectral resolution in the range 1300 < R < 2500, can be retrieved from the SDSS Catalog Archive Server. Finally, we also provide a supplemental list of an additional 4841 quasars that have been identified serendipitously outside of the superset defined to derive the main quasar catalog.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Systematic Analyzer Of Numeric Data

This system is meant to take large amounts of digital, automatically gathered data, and then organize it into a form that shows data integrity and plot, graph.

Lehmer, Jacob P.↗
DOE OSTI2023

DER Digital Supply Chain Gap Analysis

Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV have increased, cyber risk has also increased. Utility solar PV installations, however, are not required to comply with the North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) plan unless they meet a minimum generation threshold of 75 MW. Individual residential-scale solar PV deployments will not meet that generation threshold and are therefore excluded from the NERC CIP requirements. With most solar installations less than 75 MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that comprise the digital supply chain can include software, code, data, and other digital components. But as clean energy technologies advance, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Supply chain cybersecurity represents a critical area for ensuring safe operations as the U.S. moves toward a clean energy future.

cybersecurity↗
DOE OSTI2024

Automatic Digitization and Orientation of Scanned Mesh Data for Floor Plan and 3D Model Generation

This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a “pen-and-ink” style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design.

Sharma, Ritesh↗
DOE OSTI2016

Development of a CO 2 Chemical Sensor for Downhole CO 2 Monitoring in Carbon Sequestration

Geologic storage of carbon dioxide (CO 2 ) has been proposed as a viable means for reducing anthropogenic CO 2 emissions. The means for geological sequestration of CO 2 is injection of supercritical CO 2 underground, which requires the CO 2 to remain either supercritical, or in solution in the water/brine present in the underground formation. However, there are aspects of geologic sequestration that need further study, particularly in regards to safety. To date, none of the geologic sequestration locations have been tested for storage integrity under the changing stress conditions that apply to the sequestration of very large amounts of CO 2 . Establishing environmental safety and addressing public concerns require widespread monitoring of the process in the deep subsurface. In addition, studies of subsurface carbon sequestration such as flow simulations, models of underground reactions and transports require a comprehensive monitoring process to accurately characterize and understand the storage process. Real-time information about underground CO 2 movement and concentration change is highly helpful for: (1) better understanding the uncertainties present in CO 2 geologic storage; (2) improvement of simulation models; and (3) evaluation of the feasibility of geologic CO 2 storage. Current methods to monitor underground CO 2 storage include seismic, geoelectric, isotope and tracer methods, and fluid sampling analysis. However, these methods commonly resulted low resolution, high cost, and the inability to monitor continuously over the long time scales of the CO 2 storage process. A preferred way of monitoring in-situ underground CO 2 migration is to continuous measure CO 2 concentration change in brine during the carbon storage process. An approach to obtain the real time information on CO 2 concentration change in formation solution is highly demanded in carbon storage to understand the CO 2 migration subsurface and to answer the public safety problem. The objective of the study is to develop a downhole CO 2 sensor that can in-situ, continuously monitor CO 2 concentration change in deep saline. The sensor is a Severinghaus-type CO 2 sensor with small size, which renders it can be embedded in monitoring well casing or integrated with pressure/temperature transducers, enabling the development of “smart” wells. The studies included: (1) prepare and characterize metal-oxide electrodes. Test the electrodes response to pH change. Investigate different ions and brine concentration effects on the electrode’s performance. Study the stability of the electrode in brine solution; (2) fabricate a downhole CO 2 sensor with the metal-oxide electrodes prepared in the laboratory. Test the performance of the CO 2 sensor in brine solutions. Study high pressure effects on the performance of the sensor; (3) design and conduct CO 2 /brine coreflooding experiments with the CO2 sensor. Monitor CO 2 movement along the core and test the performance of the sensor in coreflooding tests. Develop a data acquisition system that can digitize the sensor’s output voltage. Our completed research has resulted in deep understanding of downhole CO 2 sensor development and CO 2 monitoring in CO 2 storage process. The developed downhole CO 2 sensor included a metal-oxide electrode, a gas-permeable membrane, a porous steel cup, and a bicarbonate-based internal electrolyte solution. Iridium oxide-based electrode was prepared and used for preparation the CO 2 sensor. The prepared iridium oxide-based electrode displayed a linearly response to pH change. Different factors such as different ions and ions concentration, temperature, and pressure effects on the electrode performance on pH response were investigated. The results indicated that the electrode exhibited a good performance even in high salt concentration of produced water. To improve the electrode performance under high pressure, IrO 2 nanoparticles with the particle size in the range of 1-2 nm were prepared and electrodeposited on stainless steel substrate by cyclic voltammetry. It was observed that the thin film of iridium oxide was formed on the substrate surface and such iridium oxide-based electrode displayed excellent performance under high pressure for longer term. A downhole CO 2 sensor with the iridium oxide-based electrode was prepared. The working principle of the CO 2 sensor is based on the measurement of the pH change of the internal electrolyte solution caused by the hydrolysis of CO 2 and then determination of the CO 2 concentration in water. The prepared downhole CO 2 sensor had the size of diameter of 0.7 in. and length of 1.5 in. The sensor was tested under the pressures of 500 psi, 2,000 psi, and 3,000 psi. A linear correlation was observed between the sensor potential change and dissolved CO 2 concentration in water. The response time of the CO 2 sensor was in the range of 60-100 minutes. Further tests indicated that the CO 2 sensor exhibited good reproducibility under high pressure. A CO 2 /brine coreflooding system was constructed to simulate the real-world CO 2 storage process. The prepared downhole CO 2 sensor was loaded in the system to monitor CO 2 movement during CO 2 /brine coreflooding test. The results indicated that the sensor could detect CO 2 movement in the tests. Further studies showed that the sensor could be recovered by brine flooding after CO 2 /brine flushed the core. The results of the coreflooding tests demonstrated that the sensor had potential application for CO 2 monitoring in carbon sequestration. A data acquisition system for the downhoe CO 2 sensor was developed and coded. The system converted the sensor output signal into digital data and transported the data from downhole to wellhead surface. The data acquisition system was tested and evaluated in the laboratory with the prepared sensor for data collection.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Transmission line relay mis-operation detection based on time-synchronized field data

In this paper, a real-time tool to detect transmission line relay mis-operation is implemented. The tool uses time-synchronized measurements obtained from both ends of the line during disturbances. The proposed fault analysis tool comes into the picture only after the protective device has operated and tripped the line. The proposed methodology is able not only to detect, classify, and locate transmission line faults, but also to accurately confirm whether the line was tripped due to a mis-operation of protective relays. The analysis report includes either detailed description of the fault type and location or detection of relay mis-operation. As such, it can be a source of very useful information to support the system restoration. The focus of the paper is on the implementation requirements that allow practical application of the methodology, which is illustrated using the field data obtained the real power system. Testing and validation is done using the field data recorded by digital fault recorders and protective relays. The test data included several hundreds of event records corresponding to both relay mis-operations and actual faults. The discussion of results addresses various challenges encountered during the implementation and validation of the presented methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Properties of Spectrally Defined Red QSOs at z = 0.3–1.2

We investigated the properties of a sample of red Quasi-stellar Objects (QSOs) using optical, radio, and infrared data. These QSOs were selected from the Sloan Digital Sky Survey Data Release 7 quasar catalog. We only selected sources with sky coverage in the Very Large Array Faint Images of the Radio Sky at Twenty-centimeters survey, and searched for sources with Wide-field Infrared Survey Explorer counterparts. We defined the spectral color of the QSOs based on the flux ratio of the rest-frame 4000 to 3000 Å continuum emission to select red QSOs and typical QSOs. In accordance with this criterion, only QSOs with redshifts between 0.3 and 1.2 could be selected. We found that red QSOs have stronger infrared emission than typical QSOs. We noted that the number ratios of red QSOs to typical QSOs decrease with increasing redshifts, although the number of typical QSOs increase with redshifts. Furthermore, at high redshifts, the luminosity distributions of typical QSOs and red QSOs seem to have similar peaks; however, at low redshifts, the luminosities of red QSOs seem to be lower than those of typical QSOs. These findings suggest that there might be at least two types of red QSOs in our QSO samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Observing correlations between dark matter accretion and galaxy growth: II. testing the impact of galaxy mass, star formation indicator, and neighbour colours

A crucial question in galaxy formation is what role new accretion has in star formation. Theoretical models have predicted a wide range of correlation strengths between halo accretion and galaxy star formation. Previously, we presented a technique to observationally constrain this correlation strength for isolated Milky Way mass galaxies at z ~ 0.12, based on the correlation between halo accretion and the density profile of neighbouring galaxies. By applying this technique to both observational data from the Sloan Digital Sky Survey and simulation data from the UNIVERSEMACHINE , where we can test different correlation strengths, we ruled out positive correlations between dark matter accretion and recent star formation activity. In this work, we expand our analysis by (1) applying our technique separately to red and blue neighbouring galaxies, which trace different infall populations, (2) correlating dark matter accretion rates with D n 4000 measurements as a longer-term quiescence indicator than instantaneous star-formation rates, and (3) analysing higher-mass isolated central galaxies with 10 11.0 < M * /M ⊙ < 10 11.5 out to z ~ 0.18. Finally, in all cases, our results are consistent with non-positive correlation strengths with ≳ 85 percent confidence, which is most consistent with models where processes such as gas recycling dominate star formation in massive z = 0 galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Reliable and redundant FPGA based read-out design in the ATLAS TileCal Demonstrator

The Tile Calorimeter at ATLAS [1] is a hadron calorimeter based on steel plates and scintillating tiles read out by PMTs. The current read-out system uses standard ADCs and custom ASICs to digitize and temporarily store the data on the detector. However, only a subset of the data is actually read out to the counting room. The on-detector electronics will be replaced around 2023. To achieve the required reliability the upgraded system will be highly redundant. Here the ASICs will be replaced with Kintex-7 FPGAs from Xilinx. This, in addition to the use of multiple 10 Gbps optical read-out links, will allow a full read-out of all detector data. Due to the higher radiation levels expected when the beam luminosity is increased, opportunities for repairs will be less frequent. The circuitry and firmware must therefore be designed for sufficiently high reliability using redundancy and radiation tolerant components. Within a year, a hybrid demonstrator including the new readout system will be installed in one slice of the ATLAS Tile Calorimeter. This will allow the proposed upgrade to be thoroughly evaluated well before the planned 2023 deployment in all slices, especially with regard to long term reliability. Different firmware strategies alongside with their integration in the demonstrator are presented in the context of high reliability protection against hardware malfunction and radiation induced errors.

ATLAS↗
DOE OSTI2016

THE EVOLUTION OF POST-STARBURST GALAXIES FROM z ∼ 1 TO THE PRESENT

Post-starburst galaxies are in the transitional stage between blue, star-forming galaxies and red, quiescent galaxies and therefore hold important clues for our understanding of galaxy evolution. In this paper, we systematically searched for and identified a large sample of post-starburst galaxies from the spectroscopic data set of the Sloan Digital Sky Survey (SDSS) Data Release 9. In total, we found more than 6000 objects with redshifts between z ∼ 0.05 and z ∼ 1.3, making this the largest sample of post-starburst galaxies in the literature. We calculated the luminosity function of the post-starburst galaxies using two uniformly selected subsamples: the SDSS main galaxy sample and the Baryon Oscillation Spectroscopic Survey CMASS sample. The luminosity functions are reasonably fit by half-Gaussian functions. The peak magnitudes shift as a function of redshift from M ∼ −23.5 at z ∼ 0.8 to M ∼ −20.3 at z ∼ 0.1. This is consistent with the downsizing trend, whereby more massive galaxies form earlier than low-mass galaxies. We compared the mass of the post-starburst stellar population found in our sample to the decline of the global star formation rate and found that only a small amount (∼1%) of all star formation quenching in the redshift range z = 0.2–0.7 results in post-starburst galaxies in the luminosity range our sample is sensitive to. Therefore, luminous post-starburst galaxies are not the place where most of the decline in the star formation rate of the universe is happening.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Advanced Tritium Process Analytics and Optimization via a Digital Twin, SRNL-TR-2023-00550

Improving the process knowledge and understanding of TCAP can be achieved by incorporating advanced tools. This project addresses “Advanced analytics for modeling, forecasting, & optimization of the tritium refinement process” and is being applied to the TCAP process. The value of digitization and advanced analytics for TCAP data are tracking of gas mixtures & inventories is presently a manual effort that is rather cumbersome. Incorporating some automation into the data capture will reduce errors and storing data in an accessible database that can be used for tracking as well as process improvements. In addition, the database approach will enable longer term history to be maintained rather than the current practice of deleting data after six months. The digitized and automated data will allow for models to be developed for the process and will enable the forecasting and optimization.

Korinko, Paul S.↗
DOE OSTI2025

Adapting Grid Criticality for Data Centers

This presentation explores the evolving definition of “critical load” in the electric grid, emphasizing the growing importance of digital infrastructure—particularly data centers—in grid resilience, restoration, and modernization. As utilities increasingly rely on AI-driven analytics and software-defined control systems, data centers have shifted from passive electricity consumers to essential computational hubs that enable National Critical Functions (NCFs) and support real-time grid operations. The deck examines the scale and impact of digital loads, the need for grid modernization to manage rapid load growth, and the diverse computing paradigms required for AI deployment. It introduces a tiered taxonomy for classifying critical loads, highlights operational dependencies between the grid and digital infrastructure, and discusses policy implications for integrating data centers into emergency planning and restoration protocols. Through case studies and practical frameworks, the presentation provides actionable insights for utilities, regulators, and planners navigating the digital transformation of the power sector.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2017

AGN Luminosity and Stellar Age: Two Missing Ingredients for AGN Unification as Seen with iPTF Supernovae

Active galactic nuclei (AGNs) are extremely powerful cosmic objects, driven by accretion of hot gas upon super-massive black holes. The zoo of AGN classes is divided into two major groups, with Type-1 AGNs displaying broad Balmer emission lines and Type-2 narrow ones. For a long time it was believed that a Type-2 AGN is a Type-1 AGN viewed through a dusty kiloparsec-sized torus, but an emerging body of observations suggests more than just the viewing angle matters. Here we report significant differences in supernova (SN) counts and classes in the first study to date of SNe near Type-1 and Type-2 AGN host galaxies, using data from the intermediate Palomar Transient Factory, the Sloan Digital Sky Survey Data Release 7, and Galaxy Zoo. We detect many more SNe in Type-2 AGN hosts (size of effect ∼5.1σ) compared to Type-1 hosts, which shows that the two classes of AGN are located inside host galaxies with different properties. In addition, Type-1 and Type-2 AGNs that are dominated by star formation according to Wide-field Infrared Survey Explorer colors m{sub W1}−m{sub W2}<0.5 and are matched in 22 μm absolute magnitude differ by a factor of ten in L[O iii] λ5007 luminosity, suggesting that when residing in similar types of host galaxies Type-1 AGNs are much more luminous. Our results demonstrate two more factors that play an important role in completing the current picture: the age of stellar populations and the AGN luminosity. This has immediate consequences for understanding the many AGN classes and galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

High-performance data management for whole slide image analysis in digital pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Wang, Xiao↗
DOE OSTI2020

Innovation in Radiological Security, Part 2 of 2 – Insights into Developing a Cloud-hosted Security Technology

A Sentry Remote Monitoring System (Sentry-RMS) is a stand-alone security system that provides detection, assessment, and communication of priority alarms as an additional means of thwarting internal and external threats to sites that maintain radiological material. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform is an optional feature of the Sentry-RMS that relays priority alarm information to the identified response stakeholders. Sentry-SECURE is hosted in a cloud environment that abstracts the data owner’s and data consumer’s platforms to allow for greater information sharing. This promotes situational awareness amongst authorized users and enables future innovation among modern response platforms. When securely architecting a cloud solution such as this, the use of design paradigms can be an effective tool to increase the accuracy and reliability of cyber- and information-security-related decisions made throughout the development process. This approach also supports the categorization of design considerations into three levels: industry concepts, project approaches, and data protections for digital processes. Industry concepts consist of the notional underpinnings that guide or motivate a security process, system, or design but often lack any tangible attributes. Project approaches represent decisions made during the design and development process to prioritize a solution, method, or practice above another that may provide a comparable functional output but lacks a desired security benefit. Data protections for digital processes represent the selection, integration, and implementation of specific controls for a given asset. This paper will explore specific examples of how Sentry-SECURE has been designed to account for considerations at each of these three levels, while balancing the operational intent of the platform with the security enhancements necessary to maintain data integrity, availability, and confidentiality.

assessment, RMS, security, physical, cloud, Cyber ↗
DOE OSTI2022

Data coverage assessment on neural network based digital twins for autonomous control system

We report in a recently developed Nearly Autonomous Management and Control (NAMAC) system, neural networks (NNs) are used to develop digital twins for diagnosis (DT-Ds). However, NNs are not usually considered extrapolation models and may result in large errors if they are applied to unseen data outside the training data (uncovered). In this study, we propose a data coverage assessment (DCA) to determine if the NN-based DT-Ds are extrapolated based on their epistemic uncertainty. The uncertainty quantification algorithms and uncertainty thresholds are selected based on the confusion matrix of classifying evaluation data into covered or uncovered data. To demonstrate the adaptability of the proposed framework, we applied it to a basic feedforward neural network and a more advanced recurrent neural network based on a more nonlinear database. Case studies show that the proposed framework can distinguish unseen data for both basic and advanced applications with proper uncertainty quantification algorithms and thresholds.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A Morphological Model to Separate Resolved–Unresolved Sources in the DESI Legacy Surveys: Application in the LS4 Alert Stream

Separating resolved and unresolved sources in large imaging surveys is a fundamental step to enable downstream science, such as searching for extragalactic transients in wide-field time-domain surveys. Here we present our method to effectively separate point sources from the resolved, extended sources in the Dark Energy Spectroscopic Instrument (DESI) Legacy Surveys (LS). We develop a supervised machine learning model based on the Gradient Boosting algorithm XGBoost. The features input to the model are purely morphological and are derived from the tabulated LS data products. We train the model using ∼2 × 10 5 LS sources in the COSMOS field with HST morphological labels and evaluate the model performance on LS sources with spectroscopic classification from the DESI Data Release 1 (∼2 × 10 7 objects) and the Sloan Digital Sky Survey Data Release 17 (∼3 × 10 6 objects), as well as on ∼2 × 10 8 Gaia stars. A significant fraction of LS sources are not observed in every LS filter, and we therefore build a “Hybrid” model as a linear combination of two XGBoost models, each containing features combining aperture flux measurements from the “blue” (gr) and “red” (iz) filters. The Hybrid model shows a reasonable balance between sensitivity and robustness, and achieves higher accuracy and flexibility compared to the LS morphological typing. With the Hybrid model, we provide classification scores for ∼3 × 10 9 LS sources, making this the largest ever machine learning catalog separating resolved and unresolved sources. The catalog has been incorporated into the real-time pipeline of the La Silla Schmidt Southern Survey (LS4), enabling the identification of extragalactic transients within the LS4 alert stream.

astrostatistics↗