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DOE OSTI2018

The Ensemble Photometric Variability of Over 10 5 Quasars in the Dark Energy Camera Legacy Survey and the Sloan Digital Sky Survey

We present the ensemble variability analysis results of quasars using the Dark Energy Camera Legacy Survey (DECaLS) and the Sloan Digital Sky Survey (SDSS) quasar catalogs. Our data set includes 119,305 quasars with redshifts up to 4.89. Combining the two data sets provides a 15 year baseline and permits the analysis of the long timescale variability. Adopting a power-law form for the variability structure function, $V=A(t/$1years)$^γ$, we use the multidimensional parametric fitting to explore the relationships between the quasar variability amplitude and a wide variety of quasar properties, including redshift (positive), bolometric luminosity (negative), rest-frame wavelength (negative), and black hole mass (uncertain). We also find that γ can be also expressed as a function of redshift (negative), bolometric luminosity (positive), rest-frame wavelength (positive), and black hole mass (positive). Tests of the fitting significance with the bootstrap method show that, even with such a large quasar sample, some correlations are marginally significant. The typical value of $γ$ for the entire data set is $≳0.25$, consistent with the results in previous studies on both the quasar ensemble variability and the structure function. A significantly negative correlation between the variability amplitude and the Eddington ratio is found, which may be explained as an effect of accretion disk instability.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

Lu, Ning↗

DOE OSTI2025

3D Continuous Forcing Dataset from 3D Constrained Variational Analysis at SGP

The continuous 3D large-scale forcing (VARANAL3D) data set derived from 3D constrained variational analysis (3DCVA) extends the conventional constrained variational analysis method by incorporating multiple sub-columns within the analysis domain. This advancement introduces spatial variability into the large-scale forcing fields, thereby enriching the data set’s applicability. The VARANAL3D data set spans from 2004 to 2018 and covers a region of 5˚×4.5˚ domain around the ARM SGP site. The analysis domain is divided into 10×9 sub-columns with 0.5˚ resolution. The 3D large-scale forcing data provides necessary variables to drive and evaluate single-column models (SCM), cloud-resolving models (CRM) ,and large-eddy simulations (LES), as well as information for testing model sensitivity to spatial variability of the large-scale forcing data, facilitating more rigorous testing and refinement of physical processes in SCM/CRM/LES.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Can We Reconcile the TA Excess and Hotspot with Auger Observations?

The Telescope Array (TA) shows a 20° hotspot as well as an excess of ultra-high-energy cosmic-rays (UHECRs) above 50 EeV when compared with the Auger spectrum. We consider the possibility that both the TA excess and hotspot are due to a dominant source in the northern sky. We carry out detailed simulations of UHECR propagation in both the intergalactic medium and the Galaxy, using different values for the intergalactic magnetic field. We consider two general classes of sources: transients and steady, adopting a mixed UHECR composition that is consistent with the one found by Auger. The spatial location of the sources is drawn randomly. We generate Auger-like and TA-like data sets from which we determine the spectrum, the sky maps, and the level of anisotropy. We find that, while steady sources are favored over transients, it is unlikely to account for all the currently available observational data. While we reproduce fairly well the Auger spectrum for the vast majority of the simulated data sets, most of the simulated data sets with a spectrum compatible with that of TA (at most a few percent depending on density model tested) show a much stronger anisotropy than the one observed. We find that the rare cases in which both the spectrum and the anisotropy are consistent require a steady source within ∼10 Mpc, to account for the flux excess, and a strong extragalactic magnetic field ∼10 nG, to reduce the excessive anisotropy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Age and gender specific biokinetic model for strontium in humans

A biokinetic model for strontium in humans is necessary for quantification of internal doses due to strontium radioisotopes. The ICRP-recommended biokinetic model for strontium has limitation for use in a population study, because it is not gender specific and does not cover all age ranges. The extensive Techa River data set on 90Sr in humans (tens of thousands of measurements) is a unique source of data on long-term strontium retention for men and women of all ages at intake. These, as well as published data, were used for evaluation of age- and gender-specific parameters for a new compartment biokinetic model for strontium (Sr-AGe model). The Sr-AGe model has similar structure as the ICRP model for the alkaline earth elements. The following parameters were mainly reevaluated: gastro-intestinal absorption and parameters related to the processes of bone formation and resorption defining calcium and strontium transfers in skeletal compartments. The Sr-AGe model satisfactorily describes available data sets on strontium retention for different kinds of intake (dietary and intravenous) at different ages (0–80 years old) and demonstrates good agreement with data sets for different ethnic groups. The Sr-AGe model can be used for dose assessment in epidemiological studies of general population exposed to ingested strontium radioisotopes.

age dependence↗

DOE OSTI2023

National Critical Minerals Data Dashboard

The Critical Minerals (CM) Data Dashboard provides data-driven insights to help unlock the potential of conventional and unconventional CM resources in the United States. The dashboard focuses on 12 CM essential for producing zero-emission transportation and clean power technology. More information about these CM and potential resources is available in our CM Story Map. Dashboard data sets can be filtered geographically using the options provided along the top of the dashboard. Data sets can also be filtered by various attributes using the options in the expandable side panel on the left side of the screen. The data sets in this dashboard were compiled from multiple sources including the USGS, USEPA, OSMRE, and DHS., Users are encouraged to provide feedback/suggestions on the function and content of the dashboard, as well as any issues that may arise.

Conventional Resources↗

DOE OSTI2025

Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling

Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average

Nguyen, Trung↗

DOE OSTI2020

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗

DOE OSTI2014

Closing the data life cycle: using information management in macrosystems ecology research

An important goal of macrosystems ecology research is to advance understanding of ecological systems at both fine and broad temporal and spatial scales. Our premise in this paper is that such projects require information management that is integrated into projects from their inception. Such efforts will lead to improved communication and sharing of knowledge among diverse project participants, better science outcomes, and more open science. We promote "closing the data life cycle" by publishing well-documented data sets, which allows for re-use of data to answer new and different questions from the ones conceived by the original projects. The practice of documenting and submitting data sets to publicly accessible data repositories ensures that research results and data are accessible to and useable by other researchers, thus fostering open science. Ecologists are often not familiar with the information management tools and requirements to effectively preserve data, however, and receive little institutional or professional incentive to do so. This paper describes recommended steps to these ends, and gives examples from current macrosystem ecology projects of why information management is so critical to ensuring that scientific results can be both reproduced and data shared for future use.

Ruegg, Janine↗

DOE OSTI2019

Liquid-bearing clouds 5-day back-trajectories

The data set contains 5-day back-trajectory calculations for cloud layers detected in the MOSAiC sounding data set. The calculations were performed by HYSPLIT informed by ERA5. In addition to trajectory coordinates and altitudes, the data set includes diagnostic and atmospheric state variables along the trajectories as well as trajectory cluster numbers associated with each airmass per Silber and Shupe,2022.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

First frontier field constraints on the cosmic star formation rate density AT z ~ 10—the impact of lensing shear on completeness of high-redshift galaxy samples

In this work, we search the complete Hubble Frontier Field data set of Abell 2744 and its parallel field for $z\sim 10$ sources to further refine the evolution of the cosmic star formation rate density (SFRD) between $z\sim 8$ and $z\sim 10$. We independently confirm two images of the recently discovered triply imaged $z\sim 9.8$ source by Zitrin et al. and set an upper limit for similar $z\sim 10$ galaxies with red colors of ${J}_{125}-{H}_{160}\gt 1.2$ in the parallel field of Abell 2744. We utilize extensive simulations to derive the effective selection volume of Lyman-break galaxies at $z\sim 10$, both in the lensed cluster field and in the adjacent parallel field. Particular care is taken to include position-dependent lensing shear to accurately account for the expected sizes and morphologies of highly magnified sources. We show that both source blending and shear reduce the completeness at a given observed magnitude in the cluster, particularly near the critical curves. These effects have a significant, but largely overlooked, impact on the detectability of high-redshift sources behind clusters, and substantially reduce the expected number of highly magnified sources. The detections and limits from both pointings result in an SFRD which is consistent within the uncertainties with previous estimates at $z\sim 10$ from blank fields. The combination of these new results with all other estimates is also consistent with a rapidly declining SFRD in the 170 Myr from $z\sim 8$ to $z\sim 10$ as predicted by cosmological simulations and dark-matter halo evolution in ΛCDM. Once biases introduced by magnification-dependent completeness are accounted for, the full six cluster and parallel Frontier Field program will be an extremely powerful new data set to probe the evolution of the galaxy population at $z\gt 8$ before the advent of the James Webb Space Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Summary Report on Synthetic Time History Development and Deployment

This report summarizes research work performed at the Colorado School of Mines (CSM) and George Washington University (GWU) in support of the DOE Office of Nuclear Energy Integrated Energy Systems (IES) program. In particular, the work focuses on improving and strengthening two fundamental steps in the mathematical and computational framework that is currently being developed to optimize the economic performance of Integrated Energy Systems. One of the innovative approaches taken by the IES program is that, rather than optimizing the system for a specific year or week or day, the optimization is performed using stochastic synthetic data. Synthetic data is generated by training artificial intelligence (AI) using available but limited data sets. The synthetic data should possess the same patterns as the original set and the same statistical properties, but never repeat the same set of values exactly. The work from CSM focuses on this first step; namely, investigating a methodology to determine the quality of the synthetic data and the right settings for properly training the AI. Once a large set of trustworthy data can be generated the problem of stochastic optimization can be tackled. The optimal IES configuration is highly dependent on the rare high peaks or deep depths of the net demand (grid electricity demand less variable renewable energy [VRE] production). Using an unbiased random sampling of the synthetic data, these extremes are very difficult to generate and therefore the optimization can be unrepresentative and inaccurate. GWU has started an approach to increase the likelihood of generating these rare events with the proper associated probabilistic weight. Both of these projects represent a step forward in achieving the capability to properly size the IESs and to properly capture the value that these systems can provide to the grid by absorbing volatility of net demand without disproportionately increasing the cost of maintaining grid reliability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

The Redmapper galaxy cluster catalog from DES science verification data

We describe updates to the redMaPPer algorithm, a photometric red-sequence cluster finder specifically designed for large photometric surveys. The updated algorithm is applied to 150 deg{sup 2} of Science Verification (SV) data from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS) DR8 photometric data set. The DES SV catalog is locally volume limited and contains 786 clusters with richness λ>20 (roughly equivalent to M{sub 500c}≳10{sup 14} h{sub 70}{sup −1} M{sub ⊙}) and 0.2<z<0.9. The DR8 catalog consists of 26,311 clusters with 0.08<z<0.6, with a sharply increasing richness threshold as a function of redshift for z≳0.35. The photometric redshift performance of both catalogs is shown to be excellent, with photometric redshift uncertainties controlled at the σ{sub z}/(1+z)∼0.01 level for z≲0.7, rising to ∼0.02 at z∼0.9 in DES SV. We make use of Chandra and XMM X-ray and South Pole Telescope Sunyaev–Zeldovich data to show that the centering performance and mass–richness scatter are consistent with expectations based on prior runs of redMaPPer on SDSS data. We also show how the redMaPPer photo-z and richness estimates are relatively insensitive to imperfect star/galaxy separation and small-scale star masks.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Creating a Tools Ecosystem for Cross-Discipline Environmental Data Reuse

Reusing data is difficult even within well-defined science communities and only gets worse when combining data from multiple communities and disciplines. Through the lens of current work on constructing an environmental epidemiological data set from multiple disciplinary sources, we demonstrate the need for a new tool ecosystem to support heterogeneous Big Data science. Extending existing community standards for schemas and/or data formats through human auditing and wrangling of the data is not feasible at scale. This work therefore suggests new approaches for the multi-disciplinary communities to build a shared tool ecosystem for big data. We discuss both the larger context of data wrangling of epidemiological data sets for novel artificial intelligence algorithms and the specific lessons from working with these multi-disciplinary data sets. Adopting a more model-driven, automatable approach promises not only better efficiency but also removes key sources of human-generated errors and promotes reuse and reproducibility of science data.

Logan, Jeremy↗

DOE OSTI2014

LandCast 2030 High-Resolution Population Projection

The LandCast 2030 data set is an empirically-informed spatial distribution of projected population of the contiguous U.S. for 2030 compiled on a 30" x 30" latitute/longitude grid. Population projections of county level numbers were developed using a modified version of the U.S. Census's projection methodology - with the U.S. Census's official projection as the benchmark. Projected census counts were apportioned to each grid cell based on locally adaptive likelihood coefficients, which are based on land cover, slope, road proximity, distances to larger cities, a moving average of current population, and other data sets. The LandCast 2030 data set was developed as part of Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk.

Source record↗

DOE OSTI2012

LONG-TERM MONITORING OF THE HIGH-ENERGY {gamma}-RAY EMISSION FROM LS I +61 Degree-Sign 303 AND LS 5039

The Fermi Large Area Telescope (LAT) reported the first definitive GeV detections of the binaries LS I +61 Degree-Sign 303 and LS 5039 in the first year after its launch in 2008 June. These detections were unambiguous as a consequence of the reduced positional uncertainty and the detection of modulated {gamma}-ray emission on the corresponding orbital periods. An analysis of new data from the LAT, comprising 30 months of observations, identifies a change in the {gamma}-ray behavior of LS I +61 Degree-Sign 303. An increase in flux is detected in 2009 March and a steady decline in the orbital flux modulation is observed. Significant emission up to 30 GeV is detected by the LAT; prior data sets led to upper limits only. Contemporaneous TeV observations no longer detected the source, or found it-in one orbit-close to periastron, far from the phases at which the source previously appeared at TeV energies. The detailed numerical simulations and models that exist within the literature do not predict or explain many of these features now observed at GeV and TeV energies. New ideas and models are needed to fully explain and understand this behavior. A detailed phase-resolved analysis of the spectral characterization of LS I +61 Degree-Sign 303 in the GeV regime ascribes a power law with an exponential cutoff spectrum along each analyzed portion of the system's orbit. The on-source exposure of LS 5039 is also substantially increased with respect to our prior publication. In this case, whereas the general {gamma}-ray properties remain consistent, the increased statistics of the current data set allows for a deeper investigation of its orbital and spectral evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

SU-F-P-32: A Phantom Study of Accuracy of Four-Dimensional Cone-Beam CT (4D-CBCT) Vs. Three-Dimensional Cone Beam CT (3D-CBCT) in Image Guided Radiotherapy

Purpose: SymmetryTM 4D IGRT system of Elekta has been installed at our institution, which offers the 4D CBCT registration option. This study is to evaluate the accuracy of 4D CBCT system by using the CIRS 4D motion phantom and to perform a feasibility study on the implementation of 4D-CBCT as image guidance for SBRT treatment. Methods: The 3D and 4D CT image data sets are acquired using the CIRS motion phantom on a Philips large bore CT simulator. The motion was set as 0.5 cm superior and inferior directions with 6 seconds recycle time. The 4D CT data were sorted as 10 phases. One identifiable part of the 4D CT QA insert from CIRS phantom was used as the target. The ITV MIP was drawn based on maximum intensity projection (MIP) and transferred as a planning structure into 4D CBCT system. Then the 3D CBCT and 4D CBCT images were taken and registered with the free breath (3D), MIP (4D) and average intensity projection (AIP)(4D) reference data sets. The couch shifts (X, Y, Z) are recorded and compared. Results: Table 1 listed the twelve couch shifts based on the registration of MIP, AIP and free breath CT data sets with 3D CBCT and 4D CBCT for both whole body and local registration. X, Y and Z represent couch shifts in the direction of the right-left, superior-inferior and anterior-posterior. The biggest differences of 0.73 cm and 0.57 cm are noted in the free breath CT data with 4D CBCT and 3D CBCT data registration. Fig. 1 and Fig. 2 are the shift analysis in diagram. Fig. 3 shows the registration. Conclusion: Significant differences exist in the shifts corresponding with the direction of target motion. Further investigations are ongoing.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Exploratory Data analysis ENvironment eXtreme scale (EDENx)

EDENx is a multivariate data visualization tool that allows interactive user driven analysis of large-scale data sets with high dimensionality. EDENx builds on our earlier system, called EDEN to enable analysis of more dimensions and larger scale data sets. EDENx provides an initial overview of summary statistics for each variable in the data set under investigation. EDENx allows the user to interact with graphical summary plots of the data to investigate subsets and their statistical associations. These plots include histograms, binned scatterplots, binned parallel coordinate plots, timeline plots, and graphical correlation indicators. From the EDENx interface, a user can select a subsample of interest and launch a more detailed data visualization via the EDEN system. EDENx is best suited for high-level, aggregate analysis tasks while EDEN is more appropriate for detail data investigations.

Steed, Chad Allen↗