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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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• Thermal ionization mass spectrometry (TIMS) is a “gold standard’ technique for actinide isotope amount ratio and assay measurements. • Ubiquitously used in: • Nuclear Nonproliferation • Nuclear Safeguards • Nuclear Forensics • Basic Science • Savannah River National Laboratory (SRNL) installed a new Thermo Scientific TRITON Plus TIMS in early 2024.
Thermal ionization mass spectrometry (TIMS) is a “gold standard’ technique for actinide isotope amount ratio and assay measurements. • Ubiquitously used in: • Nuclear Nonproliferation • Nuclear Safeguards • Nuclear Forensics • Basic Science • Savannah River National Laboratory (SRNL) installed a new Thermo Scientific TRITON Plus TIMS in early 2024.
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The 3 H(d, n) 4 He reaction is of significant interest in nuclear astrophysics and nuclear applications. It is an important, early step in big-bang nucleosynthesis and a key process in nuclear fusion reactors. We use one- and two-level R-matrix approximations to analyze data on the cross section for this reaction at center-of-mass energies below 215 keV. We critically examine the data sets using a Bayesian statistical model that allows for both common-mode and additional point-to-point un- certainties. We use Markov Chain Monte Carlo sampling to evaluate this R-matrix-plus-statistical model and find two-level R-matrix results that are stable with respect to variations in the channel radii. The S factor at 40 keV evaluates to 25.36(19) MeV b (68% credibility interval). We discuss our Bayesian analysis in detail and provide guidance for future applications of Bayesian methods to R-matrix analyses. We also discuss possible paths to further reduction of the S-factor uncertainty.
High purity germanium (HPGe) currently provides the highest readily available resolution gamma detection for a broad range of radiation measurements, but microcalorimetry is a developing option that has considerably higher resolution even than HPGe. Superior microcalorimetry resolution offers the potential to better distinguish closely spaced X-rays and gamma-rays, a common challenge for the low energy spectral region near 100 keV from special nuclear materials, and the higher signal-to-background ratio also confers an advantage in detection limit. As microcalorimetry continues to develop, it is timely to assess the impact of uncertainties in detector and item response functions and in basic nuclear data, such as branching ratios and half-lives, used to interpret spectra in terms of the contributory radioactive isotopes. We illustrate that a new inference option known as approximate Bayesian computation (ABC) is effective and convenient both for isotopic inference and for uncertainty quantification for microcalorimetry. The ABC approach opens a pathway to new and more powerful implementations for practical applications than currently available.
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CDMSlite Run 2 was a search for weakly interacting massive particles (WIMPs) with a cryogenic 600 g Ge detector operated in a high-voltage mode to optimize sensitivity to WIMPs of relatively low mass from 2 - 20 GeV/$c^2$. In this article, we present an effective field theory (EFT) analysis of the CDMSlite Run 2 data using an extended energy range and a comprehensive treatment of the expected background. A binned likelihood Bayesian analysis was performed on the recoil energy data, taking into account the parameters of the EFT interactions and optimizing the data selection with respect to the dominant background components. Energy regions within 5$\sigma$ of known activation peaks were removed from the analysis. The Bayesian evidences resulting from the different operator hypotheses show that the CDMSlite Run 2 data are consistent with the background-only models and do not allow for a signal interpretation assuming any additional EFT interaction. Consequently, upper limits on the WIMP mass and coupling-coefficient amplitudes and phases are presented for each EFT operator. These limits improve previous CDMSlite Run 2 bounds for WIMP masses above 5 GeV/$c^2$.
The KATRIN experiment aims to determine the absolute neutrino mass by measuring the endpoint region of the tritium β-spectrum. As a large-scale experiment with a sharp energy resolution, high source luminosity and low background it may also be capable of testing certain theories of neutrino interactions beyond the standard model (SM). An example of a non-SM interaction are right-handed currents mediated by right-handed W bosons in the left-right symmetric model (LRSM). In this extension of the SM, an additional SU(2){sub R} symmetry in the high-energy limit is introduced, which naturally includes sterile neutrinos and predicts the seesaw mechanism. In tritium β decay, this leads to an additional term from interference between left- and right-handed interactions, which enhances or suppresses certain regions near the endpoint of the beta spectrum. In this work, the sensitivity of KATRIN to right-handed currents is estimated for the scenario of a light sterile neutrino with a mass of some eV. This analysis has been performed with a Bayesian analysis using Markov Chain Monte Carlo (MCMC). The simulations show that, in principle, KATRIN will be able to set sterile neutrino mass-dependent limits on the interference strength. The sensitivity is significantly increased if the Q value of the β decay can be sufficiently constrained. However, the sensitivity is not high enough to improve current upper limits from right-handed W boson searches at the LHC.
This is an open source software library for performing Bayesian genome scale 13C Metabolic Flux Analysis (Bayesian 13C MFA). This is a new type of metabolic modeling that quantifies flux in a probabilistic manner at the genome scale.
The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.
The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.