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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Open Hardware in Science: The Benefits of Open Electronics

Abstract Openly shared low-cost electronic hardware applications, known as open electronics, have sparked a new open-source movement, with much untapped potential to advance scientific research. Initially designed to appeal to electronic hobbyists, open electronics have formed a global “maker” community and are increasingly used in science and industry. In this perspective article, we review the current costs and benefits of open electronics for use in scientific research ranging from the experimental to the theoretical sciences. We discuss how user-made electronic applications can help (I) individual researchers, by increasing the customization, efficiency, and scalability of experiments, while improving data quantity and quality; (II) scientific institutions, by improving access to customizable high-end technologies, sustainability, visibility, and interdisciplinary collaboration potential; and (III) the scientific community, by improving transparency and reproducibility, helping decouple research capacity from funding, increasing innovation, and improving collaboration potential among researchers and the public. We further discuss how current barriers like poor awareness, knowledge access, and time investments can be resolved by increased documentation and collaboration, and provide guidelines for academics to enter this emerging field. We highlight that open electronics are a promising and powerful tool to help scientific research to become more innovative and reproducible and offer a key practical solution to improve democratic access to science.

Oellermann, Michael (ORCID:0000000153926737)↗
DOE OSTI2021

MIGHTEE: total intensity radio continuum imaging and the COSMOS/XMM-LSS Early Science fields

ABSTRACT MIGHTEE is a galaxy evolution survey using simultaneous radio continuum, spectropolarimetry, and spectral line observations from the South African MeerKAT telescope. When complete, the survey will image ∼20 deg2 over the COSMOS, E-CDFS, ELAIS-S1, and XMM-Newton Large Scale Structure field (XMM-LSS) extragalactic deep fields with a central frequency of 1284 MHz. These were selected based on the extensive multiwavelength data sets from numerous existing and forthcoming observational campaigns. Here, we describe and validate the data processing strategy for the total intensity continuum aspect of MIGHTEE, using a single deep pointing in COSMOS (1.6 deg2) and a three-pointing mosaic in XMM-LSS (3.5 deg2). The processing includes the correction of direction-dependent effects, and results in thermal noise levels below 2 $\mathrm{\mu }$Jy beam−1 in both fields, limited in the central regions by classical confusion at ∼8 arcsec angular resolution, and meeting the survey specifications. We also produce images at ∼5 arcsec resolution that are ∼3 times shallower. The resulting image products form the basis of the Early Science continuum data release for MIGHTEE. From these images we extract catalogues containing 9896 and 20 274 radio components in COSMOS and XMM-LSS, respectively. We also process a close-packed mosaic of 14 additional pointings in COSMOS and use these in conjunction with the Early Science pointing to investigate methods for primary beam correction of broad-band radio images, an analysis that is of relevance to all full-band MeerKAT continuum observations, and wide-field interferometric imaging in general. A public release of the MIGHTEE Early Science continuum data products accompanies this article.

Heywood, I. (ORCID:0000000168645057)↗
DOE OSTI2022

An open science exploration of global 1-km simulations of the earth's atmosphere

We are making available a multi-petabyte data set for open science via a data hackathon. The participants bring their science to the computing and data facility, alleviating the need to transfer and manage the large volume of data. This accelerates the process of discovery science while increasing scientific throughput.

Anantharaj, Valentine↗
DOE OSTI2019

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science

Despite their wide and successful applications, deep learning (DL) models are prone to overfitting for small training datasets, produce a poor predictive performance for uncertain data, and provide point estimations without any indication of the accuracy and credibility. These limitations of the deterministic DL models hinder their effective application in Earth system science where the labelled data are sparse, noisy and incomplete with large uncertainty and where the predictive uncertainty quantification is needed for scientific understanding and policy decision making. Integration of Bayesian inference into DL models adds an estimate of uncertainty and regularization in the predictions. However, traditional Bayesian methods are computationally unaffordable and inflexible for high-dimensional problems, which limits their application to DL systems that typically have millions of model parameters. In this effort, we propose an efficient and general-purpose Bayesian inference method to advance DL model optimization and uncertainty quantification, so as to facilitate the adoption of DL in Earth sciences. In a demonstration, we integrate the proposed Bayesian method with a feedforward neural network (NN) to build a fast-to-evaluate surrogate of the complex Energy Exascale Earth System Land Model for efficient modeling. The formulated Bayesian NN, using a small number of training data, produces an accurate prediction with high credibility, whereas with the same small training size, the deterministic NN cannot yield a reasonable estimation and does not provide confidence information. The proposed Bayesian method is computationally efficient and flexible, capable of integration with diverse network variants such as convolutional NNs and recurrent NNs to advance the application of DL in Earth sciences.

Lu, Dan↗
DOE OSTI2024

Preparing an on-Demand Cloud Processing Workflow for NISAR Ecosystems Science Products

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance.

Christensen, Alexandra↗
DOE OSTI2024

Ecosystem Science with NISAR: Final Preparations in The Pre-Launch Period

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

Siqueira, Paul↗
DOE OSTI2016

Delivering Science on Day One

While benchmarking software is useful for testing the performance limits and stability of Argonne National Laboratory’s new Theta supercomputer, there is no substitute for running real applications to explore the system’s potential. The Argonne Leadership Computing Facility’s Theta Early Science Program, modeled after its highly successful code migration program for the Mira supercomputer, has one primary aim: to deliver science on day one. Here is a closer look at the type of science problems that will be getting early access to Theta, a next-generation machine being rolled out this year.

Williams, Timothy J.↗
DOE OSTI2020

Cross-facility science with the Superfacility Project at LBNL

As data sets from DOE user science facilities grow in both size and complexity there is an urgent need for new capabilities to transfer, analyze and manage the data underlying scientific discoveries. LBNL's Superfacility project brings together experimental and observational research instruments with computational and network facilities at the National Energy Research Scientific Computing Center (NERSC) and the Energy Sciences Network (ESnet) with the goal of enabling user science. Here, we report on recent innovations in the Superfacility project, including advanced data management, API-based automation, real-Time interactive user interfaces, and supported infrastructure for 'edge' services.

Enders, B↗
DOE OSTI2024

A Vision for the Science of Rare Isotopes

The field of nuclear science has considerably advanced since its beginning just over a century ago. Today, the science of rare isotopes is on the cusp of a new era with theoretical and computing advances complementing experimental capabilities at new facilities internationally. In this article we present a vision for the science of rare isotope beams (RIBs). We do not attempt to cover the full breadth of the field; rather, we provide a perspective and address a selection of topics that reflect our own interests and expertise. We focus in particular on systems near the drip lines, where one often finds nuclei that are referred to as exotic and where the role of the nuclear continuum is only just starting to be explored. An important aspect of this article is its attempt to highlight the crucial connections between nuclear structure and the nuclear reactions required to fully interpret and leverage the rich data to be collected in the next years at RIB facilities. Further, we connect the efforts in structure and reactions to key questions of nuclear astrophysics.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2019

ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists

Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.

Sangkeun, MattLee↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2021

2020 ETI Annual Summer School: Data Science and Engineering

The Consortium for Enabling Technologies & Innovation (ETI) was established in 2019 to address emerging technologies within the context of nuclear nonproliferation. ETI creates a research and education environment to support cross-cutting technologies across three core disciplines: 1) computer and engineering science research specifically in a form of machine learning and high performance computing (HPC), 2) advanced manufacturing, and 3) nuclear detection technologies. For outreach and development, ETI hosted the first of three summer schools from August 24-28, 2020 with the theme of “Data Science and Engineering”. The school was hosted in an on-line format and had over 200 participants. The recorded content is available on-line as a resource for students. The summer school had four modules: 1) Fundamentals of data Applications, 2) Computational Machine Learning, 3) Bayesian Modeling and Inference, and 4) Data Science for Safeguards. Modules contained both lectures as well as student exercises. Poll Everywhere was utilized in some modules as an on-line method to engage large groups of students. Upcoming ETI Summer Schools include Novel Instrumentation in 2021 and Advanced Manufacturing in 2022.

Biegalski, Steven R.↗
DOE OSTI2022

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend on the science goals. We illustrate the utility of this suite by showing examples for the validation of cosmoDC2, the extragalactic catalog recently released for the LSST DESC second Data Challenge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

NCSE's 13th National Conference on Disasters and Environment: Science, Preparedness and Resilience, Post Conference Follow-up Activities and Dissemination

The National Council for Science and the Environment (NCSE) received $15,000 from the US Department of Energy to support post-conference activities of the 13th National Conference on the theme of Disasters and the Environment: Science, Preparedness and Resilience, held on January 15-17, 2013 at the Ronald Reagan Building and International Trade Center in Washington, DC. Over 1,000 participants from the scientific, emergency response, policy, conservation, and business communities, as well as federal and local government officials, and international entities attended the event. The conference developed actionable outcomes that constructively advance the science behind decision-making on environmental disasters, with an intended result of more prepared and resilient communities in light of a changing climate. Disasters and Environment topic was addressed through six organizing themes: Cascading Disasters; Intersection of the Built and Natural Environments; Disasters as Mechanisms of Ecosystem Change; Rethinking Recovery and Expanding the Vision of Mitigation; Human Behavior and its Consequences; and "No Regrets" Resilience. The program featured eight plenary sessions, 24 symposia and 23 breakout workshops and addressed pivotal issues surrounding disasters and environment including lifeline services, the energy, climate, hazard nexus, grid collapse, community vulnerability, and natural resource management. Sessions, symposia and workshops were conducted by over 200 distinguished thought leaders, scientists, government officials, policy experts and international speakers throughout the three day event. Following the conference, NCSE prepared a set of recommendations and results from the workshops and disseminated the results to universities, organizations and agencies, the business community. NCSE’s national dissemination involved organized several targeted trips and meetings to disseminate significant findings to key stakeholder groups.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Multiscale Computation: Needs and Opportunities for BER Science

The Environmental Molecular Sciences Laboratory (EMSL), a scientific user facility managed by Pacific Northwest National Laboratory for the U.S. Department of Energy, Office of Biological and Environmental Research (BER), conducted a one-day workshop on August 26, 2014 on the topic of “Multiscale Computation: Needs and Opportunities for BER Science.” Twenty invited participants, from various computational disciplines within the BER program research areas, were charged with the following objectives: Identify BER-relevant models and their potential cross-scale linkages that could be exploited to better connect molecular-scale research to BER research at larger scales, and Identify critical science directions that will motivate EMSL decisions regarding future computational (hardware and software) architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Development of Innovative Radioactive Isotope Production Techniques at the Pennsylvania State University Radiation Science and Engineering Center

The Penn State Breazeale Nuclear Reactor (PSBR) at the Radiation Science and Engineering Center (RSEC) has produced radioisotopes for research and commercial purposes since 1956. With the rebirth of the radiochemistry education and research program at the RSEC, the Center stands poised to produce a variety of radioisotopes for research and industrial work that is in line with the mission of the DOE Office of Science, Office of Nuclear Physics, Isotope Development and Production Research and Application Program. The RSEC received funding from the Office of Science in 2010 to improve production techniques and develop new capabilities. Under this program, we improved our existing techniques to provide four radioisotopes (Mn-56, Br-82, Na-24, and Ar-41) to researchers and industry in a safe and efficient manner. The RSEC is also working to develop new innovative techniques to provide isotopes in short supply to researchers and others in the scientific community, specifically Cu-64 and Cu-67. Improving our existing radioisotopes production techniques and investigating new and innovative methods are two of the main initiatives of the radiochemistry research program at the RSEC.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Harnessing Relativistic Laser Plasmas to Generate Intense Ion Beams: A Plasma Science Frontier White Paper

Optical lasers with high intensity (> 10 19 W/cm 2 ), high power (> 100 TW) and high energy (> 50 J) are enabling research that is expanding the frontiers of physics. Some of the research opportunities and importance are described in several reports. Here we highlight a prominent one: harnessing relativistic laser plasmas to generate intense ion beams, much more intense than it is possible with conventional particle-acceleration technology. This topic is important in two ways. On the one hand, it represents exceedingly exciting science in plasma physics and extreme electrodynamics (i.e., “high-field” physics). On the other hand, it enables advanced technology for advancing other fields of science, for societal applications (e.g., fast ignition8) and for national defense. There are tremendous opportunities for continued progress in existing laser facilities in the US (with laser intensities up to ~ 10 21 W/cm 2 , such as the Trident Laser Facility) provided modest funding support and reorientation of these lasers as national user facilities. Longer term, there are also great scientific opportunities for the US by extending our capabilities to intensities of ~ 10 22 W/cm 2 and beyond with shaped laser pulses of intermediate duration (~ 100 fs), to complement high intensity, shorter pulse length lasers planned worldwide. Such a complementary US facility would in fact excel in the ion-beam generation mission proposed here. At ~ 10 22 W/cm 2 and beyond, besides meeting the requirements for important societal applications of intense ion beams, novel high-field physics becomes important and amenable to study. That includes e.g., radiation self-force for electrons, and direct coupling of the laser to the ions (not just to electrons and then indirectly to the ions). These new physics in turn may open new technological opportunities, such as x-ray and gamma-ray-beam sources and new ion-acceleration mechanisms. It is hard to overstate the impact of particle accelerators starting in the last century to the present. Accelerators have had profound impact in society, both as a key enabling experimental tool for the modern scientific edifice, as well as for societal applications such as cancer therapy. Conventional accelerator technology is bumping against two main limitations. One is particle energy (at a reasonable size and cost) rooted in the ~ 25 MeV/m gradient limit from electrical breakdown of conventional accelerator structures. The second is beam intensity, limited by current and space-charge stability of conventional (non-neutral) particle beams. The former issue is being addressed in ingenious ways by “dielectric accelerators” which use high-power, low-energy lasers to set up transient-high fields in dielectric microstructures. These devices promise to advance the state of the art in acceleration gradients, but of course cannot address the intensity limitations. Ultimately, the highest performance is going to require a plasma as the acceleration medium. Plasmas can support the highest accelerating potential gradients and the highest currents. Moreover, by delivering neutralized beams (ions co-moving with unbound electrons), the usual current and space-charge limits can be circumvented. Realizing the highest ion energies and intensities requires a high-power density source to drive the system. Intense, high-power, high-energy lasers represent an ideal drive source, provided the physics can be understood and controlled, i.e., a challenge proposed here. To meet that challenge, the science community can build on the significant progress over the last decade, experimentally, computationally and theoretically.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids

The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively with advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research project demonstrate significant advances in all six research areas. The major conclusions are that statically-adaptive variable-resolution modeling is currently becoming mature in the climate sciences, and that AMR holds outstanding promise for future-generation weather and climate models on high-performance computing architectures.

58 GEOSCIENCES↗