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DOE OSTI2022

Estimating List-Mode Data Sensitivities to Nuclear Data with MCNP6

Nuclear data are a vital component of predictive simulations used in applications like experiment design, stockpile stewardship, nuclear nonproliferation/safeguards, health physics, and criticality safety. A singular simulation requires the coalescence of different areas of nuclear data such as cross sections, angular distributions, and energy distributions of emitted neutrons for different materials and energy ranges. Improving nuclear data and thus reducing the uncertainty in simulated parameters could enable smaller, better-informed safety factors and ultimately reduce operational and procedural costs. There is a constant effort to garner a better understanding of the physical quantities represented by nuclear data through experiments. Integral experiment benchmarks use simulated and measured results to validate current nuclear data values. In the past, benchmarks primarily focused on the effective multiplication factor (k eff ); however, this limited scope has caused compensating errors and areas of nuclear data that lack validation. Compensating errors are inaccuracies in nuclear data that are obfuscated by cancellation when observing integrated values such as k eff . Diverse integral benchmark experiments that look for quantities of interest other than k eff and include multiple responses minimize the possibility of compensating errors and provides validation to areas of nuclear data previously lacking experimental validation. Benchmark experiments can be optimized during the design process to be highly dependent on specific areas of nuclear data. The dependence of a response in an experiment to a specific area/type of nuclear data is defined as sensitivity. A larger sensitivity means that nuclear data uncertainties will play a larger role in the response(s) resulting in larger bias. Currently, the sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®1 ) transport code are limited to responses of k eff and tallied values (e.g., flux, surface current). As a part of the EUCLID project, this work explores estimating list-mode nuclear data sensitivities that can be used to design experiments aimed to constrain and reduce compensating errors in nuclear data by focusing on responses other than k eff . Tallied values are ideal quantities that are estimated with detectors during experiments. List-mode data (a list of neutron collection times) are the direct output of detector systems in subcritical neutron noise experiments. Expanding MCNP sensitivity capabilities to include the sensitivity of responses estimated from list-mode data, such as the prompt neutron decay constant (α) and multiplicity estimates (S and D), enables more direct comparison of simulated and measured experimental quantities. Additionally, deterministic tools such as SENSMG are capable of obtaining sensitivities to a wide variety of responses; however, these tools cannot handle complex geometries due to the assumptions made in discretizing the phase-space variables of the Boltzman transport equation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Phonon-mediated quantum efficiency measurement in semiconductors

Accurate quantum efficiency measurement not only provides crucial information for the photovoltaic cell industry but also supports experiments aimed at directly detecting dark matter and elastic neutrino interactions. The dark matter direct searches paradigm has recently expanded to include particles with masses below 1,MeV/c2, where the expected signal in an electron–recoil interaction is approximately in the eV range, just above the energy gap for silicon and germanium. A robust calibration method for ionization signals in this lower energy region is essential. This paper presents a method for measuring quantum efficiency and yield (q/E) in semiconductors using phonon-mediated calorimetry. The Neganov–Trofimov–Luke phonon amplification method in low-temperature semiconductor crystals has been employed to indirectly measure ionization down to single-electron accuracy. Specifically, at zero bias, the phonon readout directly quantifies the total energy deposited within the detector, independent of the ionization yield. This eliminates a significant source of systematic uncertainty in quantum efficiency estimates associated with total energy uncertainty. The paper includes results from an updated ionization efficiency measurement in a germanium detector.

Physics↗

DOE OSTI2023

Correlated Signal Analysis for Nuclear Emergency Response

In the context of nuclear emergency response (NER) scenarios, the critical task is to quickly identify a "black box" as a potential threat, as failing to do so could have catastrophic consequences. Techniques for passive assay of a “black box” typically include gamma-ray spectroscopy and neutron coincidence/multiplicity counting. However, there are significant challenges associated with these type of measurements. First, the presence of intervening materials can obstruct the detection of relevant signatures. Second, the presence of strong non-fission neutron sources, like (α, n) emitters, can add uncertainties to the neutron multiplicity analysis. In our LDRD-MFR Phase II work, a portable neutron spectrometer, called the Compact Fast Neutron Spectrometer (CFNS), was developed for NER applications. The CFNS system leverages information-rich neutron energy spectra to derive actionable information. This work investigates the use of correlated signals in the CFNS from special nuclear material (SNM) to characterize physical properties, such as intervening shielding material and fission to non-fission neutron contributions. In this report, the Phase II results from bulk SNM measurements at the National Criticality Experiments Research Center (NCERC) are briefly discussed along with the motivation for this work. Afterwards, simulations using the MCNPX-PoliMi transport code are discussed, which were used to expand our correlated signal study. Signal triggered analysis for neutron multiplicity extraction will also be discussed. Lastly, the use neutron-photon correlations for intervening material identification are shown, along with the use of correlated neutron energy spectra for α-ratio extraction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

X-ray continuum spectroscopy of inertial confinement fusion implosions at the National Ignition Facility

A methodology for measuring x-ray continuum spectra of inertial confinement fusion (ICF) implosions is described. The method relies on the use of ConSpec, a high-throughput spectrometer using a highly annealed pyrolytic graphite crystal , which measures the spectra in the ≃20–30 keV range. Due to its conical shape, the crystal is sagittally focusing a Bragg-reflected x-ray spectrum into a line, which enhances the recorded x-ray emission signal above the high neutron-induced background accompanying ICF implosions at the National Ignition Facility. To improve the overall measurement accuracy, the sensitivity of the spectrometer measured in an off-line x-ray laboratory setting was revised. The error analysis was expanded to include the accuracy of the off-line measurements, the effect of the neutron-induced background, as well as the influence of possible errors in alignment of the instrument to the ICF target. Here we demonstrate how the improved methodology is applied in the analysis of ConSpec data with examples of a relatively low-neutron-yield implosion using a tritium–hydrogen–deuterium mix as a fuel and a high-yield deuterium–tritium (DT) implosion producing high level of the background. In both cases, the shape of the measured spectrum agrees with the exponentially decaying spectral shape of bremsstrahlung emission to within ±10%. In the case of the high-yield DT experiment, non-monotonic deviations slightly exceeding the measurement uncertainties are observed and discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Interseismic Strain Accumulation between the Colorado Plateau and the Eastern California Shear Zone: Implications for the Seismic Hazard near Las Vegas, Nevada

ABSTRACT Crustal deformation in the central Basin and Range between the Colorado plateau and the Eastern California Shear Zone is active but slow, making it a challenge to assess how strain is distributed and crustal motion transferred. However, knowledge of strain rates is very important, particularly for addressing the seismic hazard for both the Las Vegas urban area and the site of the proposed Yucca Mountain nuclear waste repository, in southern Nevada. Global Positioning System (GPS) data provide important constraints, particularly now that the GPS network in the area has substantially expanded in recent years. However, because deformation is slow, it is important to mitigate any transient tectonic and nontectonic signals to obtain the most accurate long-term interseismic motion and robust estimation of strain rates. We use data from all GPS stations in the region including both long-running continuous and semicontinuous stations. We model and remove postseismic displacements at these stations using source parameters for 41 events, dating back to the 1700 Cascadia megathrust earthquake, which contribute significantly to the deformation field within the central Basin and Range. We also remove correlated noise from the time series with the common-mode component imaging technique. We find that removal of both the postseismic transients and common-mode noise substantially reduces the uncertainties and spatial variation in the velocities. We find east–west extension across the Las Vegas Valley of 0.5–0.6 mm/yr. The interseismic strain rate field, calculated with the final velocities, reveals higher strain rates through southern Nevada than in previous studies, with rates within Las Vegas Valley of 8.5±2.4×10−9 yr−1. Our results also confirm shear along the Pahranagat shear zone, but the estimated amplitude is strongly affected by postseismic relaxation.

Geochemistry & Geophysics↗

DOE OSTI2021

The soil health assessment protocol and evaluation applied to soil organic carbon

The concept of soil health has evolved over the past several decades, recognizing that dynamic soil property response to management and land use is highly dependent on site-specific factors that must be considered when interpreting soil health measurements. Initially, the Soil Management Assessment Framework (SMAF) and Comprehensive Assessment of Soil Health (CASH) were developed and used globally for scoring soil health indicators. However, both SMAF and CASH frameworks were developed using a relatively small dataset and their interpretation curves were not validated at the nationwide scale. Expanding upon these concepts, we propose the Soil Health Assessment Protocol and Evaluation (SHAPE) tool. The SHAPE was developed using 14,680 soil organic C (SOC) observations from across the United States, and accounts for edaphic and climate factors at the continental scale. Data were compiled from the literature, the Cornell Soil Health Laboratory, and the Kellogg Soil Survey Laboratory. In this approach, scoring curves are Bayesian model-based estimates of the conditional cumulative distribution function (CDF) for defined soil peer groups reflecting five soil texture and five soil suborder classes adjusted for mean annual temperature and precipitation. Specifically, SHAPE produces scores between 0 and 1 (0–100%) for measured SOC values that reflect the quantile or position within the conditional CDF along with measures of uncertainty. Herein, we focus on development of the SHAPE scoring curve for SOC with our case studies. SHAPE is a flexible, quantitative tool that provides a regionally relevant interpretation of this key soil health indicator.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Computational materials assessment of the D/Li-stripping neutron source as a prototypical facility for fusion materials testing

As the US fusion materials community awaits the selection and design of a fusion prototypical neutron source (FPNS), a risk reduction exercise has been conducted to (i) provide an updated materials performance evaluation using state-of-the-art computational materials modeling, (ii) expand on legacy analysis based on pure Fe to other relevant fusion structural materials types, and (iii) ensure that materials response under FPNS operational conditions is consistent with referential fusion reactor conditions. The current paper describes the efforts undertaken to assemble a comprehensive computational methodology that includes neutronics, primary damage calculations, atomistic simulations of displacement cascades, chemical inventory evolution calculations, and a computational thermodynamic analysis of emerging phases during irradiation. Our work extends existing studies in pure Fe to reduced-activation ferritic/martensitic steels, tungsten, silicon carbide, and vanadium alloys. We focus on the single-beam deuteron/lithium-stripping neutron source behind the IFMIF-DONES concept, which we assess against ITER, two DEMO designs, and an ideal pure 14-MeV flux. Our analysis indicates that, within standard uncertainties inherent to the models employed, the DONES concept adequately captures fusion conditions in the four materials analyzed. Our work is intended as a comprehensive irradiation damage analysis of fusion-representative neutron sources, to be used for further neutron source evaluation and fusion facility operation.

Marian, Jaime [Univ. of California, Los Angeles, C↗

DOE OSTI2025

Non-stationary precipitation design standards for stormwater infrastructure modernization at USAF installations

The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.

Intensity-duration-frequency curves↗

DOE OSTI2022

Reference surface excess isotherms for carbon dioxide adsorption on ammonium ZSM-5 at various temperatures

Abstract This work is part of the effort at the Facility for Adsorbent Characterization and Testing (FACT lab) at the National Institute of Standards and Technology (NIST) to develop reference materials, reference data, and measurement best practices for adsorption metrology. High-pressure surface excess isotherms for CO 2 adsorption on NIST Reference Material 8852 (ammonium ZSM-5) at 15 °C, 25 °C, and 35 °C are reported, expanding on a FACT Lab-organized international interlaboratory study on this sorbent/sorbate pair at 20 °C. The range of temperatures of the present study is of interest for many CO 2 adsorption measurements and applications. Measurements were made using five different adsorption instruments, both manometric and gravimetric. Excellent agreement in the measured isotherms among the instruments was found. An empirical reference equation of the form, $${n}_{ex,ref}({P}_{eq})=\frac{c}{\{{1+\mathrm{exp}[\left(-\mathrm{ln}\left(P\right)+a\right)/b ]\}}^{b }} ,$$ n e x , r e f ( P eq ) = c { 1 + exp [ - ln P + a / b ] } b , [ n ex,ref is surface excess uptake (mmol/g); P eq is equilibrium pressure (MPa); P is ( P eq )/(1 MPa)]; a , b , c are constants] and the 95% uncertainty interval were determined for the isotherms at each of the temperatures. Lastly, the isosteric heat of adsorption is estimated from absolute isotherms derived from the surface excess reference data.

Nguyen, Huong Giang T. (ORCID:0000000210527565)↗

DOE OSTI2024

The role of stereochemistry in combustion processes

Stereochemical effects significantly influence chemical processes, yet it is not well understood if they are a leading source of uncertainty in combustion modeling. Stereochemistry influences a combustion model (i) at the earliest stage of its construction when mapping the reaction network, (ii) in the computation of individual thermochemical and rate parameters, and (iii) in the prediction of combustion observables. The present work reviews the importance of enumerating stereochemical species and reactions at each of these steps. Further, it analyzes the separate influence of several types of stereochemistry, including geometric, optical, and fleeting transition state diastereomers. Three reaction networks serve to examine which stages of low-temperature oxidation are most affected by stereochemistry, including the first and second oxidation of n-butane, the third oxidation of n-pentane, and the early stages of pyrolysis of 1- and 2-pentene. The 149 reactions in the n-butane mechanism are expanded to 183 reactions when accounting for diastereomerism. Each of these 183 reactions is parameterized with ab initio kinetics computations to determine that, for the n-butane mechanism, the median factor of diastereomeric deviation is 3.5 at 360 K for rate constants and as high as 1.6 for mechanism reactivity, in terms of ignition delay times, as opposed to a mechanism without stereochemical expansion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 5 and Phase 6

This report summarizes the data collected on two test matrices of low-activity waste (LAW) glass compositions intended to expand the composition-property database: Low-Activity Waste (LAW) Phase 5: Expansion of LAW Glass Composition Boundaries and LAW Phase 6: High PCT and VHT Response Glass Matrix. Both matrix glass compositions were statistically designed to expand the LAW glass composition region. The analyses performed on these glasses include chemical composition (for target compositional verification), density, viscosity, electrical conductivity, crystal fraction, container centerline cooling with crystal identification, the product consistency test (PCT) response, the vapor hydration test (VHT) response, and sulfur solubility. Because of the slightly different scope of the two matrices, not all methods were applied to both matrices. Specifically, the following measurements were taken only on the LAW Phase 5: Expansion of LAW Glass Composition Boundaries glasses: crystal fraction as a function of temperature, density (ρ), viscosity (η), and electrical conductivity (EC, e). Combined, these data contribute a significant amount, 51 glasses, to the database for high LAW loaded enhanced waste glasses. Most of these data are focused near the boundaries of acceptable PCT and VHT responses, where prediction uncertainties are most impactful.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

42 ENGINEERING↗

DOE OSTI2022

J-PLUS: Support vector regression to measure stellar parameters

Stellar parameters are among the most important characteristics in studies of stars which, in traditional methods, are based on atmosphere models. However, time, cost, and brightness limits restrain the efficiency of spectral observations. The Javalambre Photometric Local Universe Survey (J-PLUS) is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools for efficiently analyzing large data sets, such as the one from J-PLUS, and enables us to expand the research domain to stellar parameters. The main goal of this study is to construct a support vector regression (SVR) algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. The training data for the parameter's regressions are featured with 12-waveband photometry from J-PLUS and are crossidentified with spectrum-based catalogs. These catalogs are from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Apache Point Observatory Galactic Evolution Experiment, and the Sloan Extension for Galactic Understanding and Exploration. We then label them with the stellar effective temperature, the surface gravity, and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach, in order to fully take into account, the uncertainties of both the magnitudes and the stellar parameters. The method utilizes more than 200 models to apply the uncertainty analysis. We present a catalog of 2 493 424 stars with the root mean square error of 160 K in the effective temperature regression, 0.35 in the surface gravity regression, and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Grad–Shafranov equilibria via data-free physics informed neural networks

A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics. In this paper, we explore a method for solving the Grad–Shafranov equation by using physics-informed neural networks (PINNs). For PINNs, we optimize neural networks by directly minimizing the residual of the partial differential equation as a loss function. We show that PINNs can accurately and effectively solve the Grad–Shafranov equation with several different boundary conditions, making it more flexible than traditional solvers. This method is flexible as it does not require any mesh and basis choice, thereby streamlining the computational process. We also explore the parameter space by varying the size of the model, the learning rate, and boundary conditions to map various tradeoffs such as between reconstruction error and computational speed. Additionally, we introduce a parameterized PINN framework, expanding the input space to include variables such as pressure, aspect ratio, elongation, and triangularity in order to handle a broader range of plasma scenarios within a single network. Parameterized PINNs could be used in future work to solve inverse problems such as shape optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

CATS: The Hubble Constant from Standardized TRGB and Type Ia Supernova Measurements

The tip of the red giant branch (TRGB) provides a luminous standard candle for constructing distance ladders to measure the Hubble constant. In practice, its measurements via edge-detection response (EDR) are complicated by the apparent fuzziness of the tip and the multipeak landscape of the EDR. Previously, we optimized an unsupervised algorithm, Comparative Analysis of TRGBs, to minimize the variance among multiple halo fields per host without relying on individualized choices, achieving state-of-the-art ~<0.05 mag distance measures for optimal data. Here we apply this algorithm to an expanded sample of SN Ia hosts to standardize these to multiple fields in the geometric anchor, NGC 4258. In concert with the Pantheon+ SN Ia sample, this analysis produces a (baseline) result of H 0 = 73.22 ± 2.06 km s -1 Mpc -1 . The largest difference in H 0 between this and similar studies employing the TRGB derives from corrections for SN survey differences and local flows used in the most recent SN Ia compilations that were absent in earlier studies. The SN-related differences total ~2.0 km s -1 Mpc -1 . A smaller share, ~1.4 km s -1 Mpc -1 , results from the inhomogeneity of the TRGB calibration across the distance ladder. We employ a grid of 108 variants around the optimal TRGB algorithm and find that the median of the variants is 72.94 ± 1.98 km s -1 Mpc -1 with an additional uncertainty due to algorithm choices of 0.83 km s -1 Mpc -1 . None of these TRGB variants result in an H 0 of less than 71.6 km s -1 Mpc -1 .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Transverse-momentum and event-shape dependence of D-meson flow harmonics in Pb–Pb collisions at s NN = 5.02 TeV

The elliptic and triangular flow coefficients v 2 and v 3 of prompt D 0 , D + , and D * + mesons were measured at midrapidity ( | y | < 0.8 ) in Pb–Pb collisions at the centre-of-mass energy per nucleon pair of s NN = 5.02 TeV with the ALICE detector at the LHC. The D mesons were reconstructed via their hadronic decays in the transverse momentum interval 1 < p T < 36 GeV / c in central (0–10%) and semi-central (30–50%) collisions. Compared to pions, protons, and J / ψ mesons, the average D-meson v n harmonics are compatible within uncertainties with a mass hierarchy for p T ≲ 3 GeV / c , and are similar to those of charged pions for higher p T . The coupling of the charm quark to the light quarks in the underlying medium is further investigated with the application of the event-shape engineering (ESE) technique to the D-meson v 2 and p T -differential yields. The D-meson v 2 is correlated with average bulk elliptic flow in both central and semi-central collisions. Within the current precision, the ratios of per-event D-meson yields in the ESE-selected and unbiased samples are found to be compatible with unity. All the measurements are found to be reasonably well described by theoretical calculations including the effects of charm-quark transport and the recombination of charm quarks with light quarks in a hydrodynamically expanding medium.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

δ 13 C methane source signatures from tropical wetland and rice field emissions

The atmospheric methane (CH 4 ) burden is rising sharply, but the causes are still not well understood. One factor of uncertainty is the importance of tropical CH 4 emissions into the global mix. Isotopic signatures of major sources remain poorly constrained, despite their usefulness in constraining the global methane budget. Here, a collection of new δ 13 C CH 4 signatures is presented for a range of tropical wetlands and rice fields determined from air samples collected during campaigns from 2016 to 2020. Long-term monitoring of δ 13 C CH 4 in ambient air has been conducted at the Chacaltaya observatory, Bolivia and Southern Botswana. Both long-term records are dominated by biogenic CH 4 sources, with isotopic signatures expected from wetland sources. From the longer-term Bolivian record, a seasonal isotopic shift is observed corresponding to wetland extent suggesting that there is input of relatively isotopically light CH 4 to the atmosphere during periods of reduced wetland extent. This new data expands the geographical extent and range of measurements of tropical wetland and rice δ 13 C CH 4 sources and hints at significant seasonal variation in tropical wetland δ 13 C CH 4 signatures which may be important to capture in future global and regional models.

54 ENVIRONMENTAL SCIENCES↗