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DOE OSTI2020

Synthetic Ancillary Service Generator (SynAS) v1

The balance of demand and supply in the electric power grid is a crucial element to grid reliability. While balancing is typically provided by conventional power plants, with spinning generators, distributed energy resources (DER) and especially electric vehicles (EVs) pose a great potential to provide such service. This project conducted a field demonstration at the Los Angeles Air Force Base (LA-AFB) where a fleet of mixed-use bi-directional EVs were actively participating in the California Independent System Operator (CAISO) market for frequency regulation. While participation in regulation markets is promising, a major drawback in designing control systems and assessing potential revenue is the lack of example frequency regulation signals. This project introduces an open-source dataset for 143 days of four-second regulation data, and the SynAS Python module to synthetically generate frequency regulation signals of arbitrary length.

Black, DouglasR↗

DOE OSTI2026

Life Cycle Analysis of Growing Canola for Biofuel Production in the United States

This study quantifies and compares the life cycle greenhouse gas (GHG) emissions of renewable diesel (RD), sustainable aviation fuel (SAF), and biodiesel (BD) produced from two U.S. canola production systems: 1) emerging intermediate winter canola, typically grown in double- or relay-cropping systems between the growing seasons of main crops, and 2) main canola, mostly spring canola but also including winter canola, which are grown as primary crops occupying the field for a full growing season. Using the Research and Development version of the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) model and the most up-to-date life cycle inventory data─field trial data for intermediate winter canola (>37,000 acres) and recent national survey data for spring canola─this life cycle analysis (LCA) estimates the direct emissions from canola cultivation and harvest, the conversion of canola into fuels, fuel transportation, and combustion. In addition, we account for market-mediated emissions associated with a scenario of 0.5 billion gallons per year of spring canola-based biofuels, including induced land use change (ILUC), induced other crop (nonfeedstock) production changes, and induced livestock production changes. For intermediate winter canola, these market-mediated effects were not modeled, as ILUC is expected to be negligible due to its integration into existing rotations, and data are currently insufficient to reliably quantify other market-mediated changes. The estimated life cycle direct emissions of RD/SAF derived from intermediate winter canola and main spring canola are about 32 and 33 g of CO2-equivalent per megajoule of fuel (g CO 2 e/MJ), respectively. Corresponding emissions for BD from intermediate winter canola and main spring canola are about 30 and 31 g of CO 2 e/MJ, respectively. Farming is the dominant emissions source for both canola systems, with intermediate winter canola and main spring canola emitting about 19 and 20 g of CO 2 e/MJ, respectively. ILUC and other induced changes increase emissions of main spring canola-derived RD/SAF and BD by about 18 and 17 g of CO 2 e/MJ, respectively. These results indicate that the GHG emissions of biofuels produced from the two canola systems may differ substantially due to the different land use dynamics of the systems.

biodiesel↗

DOE OSTI2024

Overview of preparation for the American WAKE ExperimeNt (AWAKEN)

The American WAKE ExperimeNt (AWAKEN) is a multi-institutional field campaign focused on gathering critical observations of wind farm–atmosphere interactions. These interactions are responsible for a large portion of the uncertainty in wind plant modeling tools that are used to represent wind plant performance both prior to construction and during operation and can negatively impact wind energy profitability. The AWAKEN field campaign will provide data for validation, ultimately improving modeling and lowering these uncertainties. The field campaign is designed to address seven testable hypotheses through the analysis of the observations collected by numerous instruments at 13 ground-based locations and on five wind turbines. The location of the field campaign in Northern Oklahoma was chosen to leverage existing observational facilities operated by the U.S. Department of Energy Atmospheric Radiation Measurement program in close proximity to five operating wind plants. The vast majority of the observations from the experiment are publicly available to researchers and industry members worldwide, which the authors hope will advance the state of the science for wind plants and lead to lower cost and increased reliability of wind energy systems.

17 WIND ENERGY↗

DOE OSTI2021

Nuclear charge densities in spherical and deformed nuclei: Toward precise calculations of charge radii

Precise measurements of atomic transitions affected by electron-nucleus hyperfine interactions offer sensitivity to explore basic properties of the atomic nucleus and study fundamental symmetries, including the search for new physics beyond the standard model of particle physics. Specifically, such measurements, augmented by atomic and nuclear calculations, will enable extraction of the higher-order radial moments of the charge-density distribution in spherical and deformed nuclei. The new data impose higher precision requirements on a theoretical description. The nuclear charge density is composed of the proton point distribution folded with the nucleonic charge distributions. The latter induce subtle relativistic corrections due to the coupling of nucleon magnetic moments with the nuclear spin-orbit density. Additional corrections come from the effect of center-of-mass projection. We assess the precision of nuclear charge density calculations by studying the behavior of relativistic and center-of-mass motion corrections to the second and fourth charge radial moments. Special attention has been paid to the magnetic spin-orbit density associated with the local variations of the spin-orbit current. The calculations for semimagic and open-shell nuclei are performed in the framework of self-consistent mean-field theory using quantified energy density functionals and density-dependent pairing forces. We used the general expression for the spin-orbit form factor that is valid for spherical and deformed nuclei. We studied the impact of various correction terms on the charge radii, fourth radial moments, diffraction radii, and surface thickness of spherical and deformed nuclei. The spin-orbit corrections to charge radial moments and surface thickness show strong shell fluctuations which can make an appreciable effect when aiming at high-precision predictions of isotopic shifts. The inclusion of relativistic and center-of-mass corrections impacts the quality of energy density functionals optimized to charge radii data. To establish reliable constraints on the existence of new forces from isotope shift measurements, precise calculations of nuclear charge densities of deformed nuclei are needed. The proper inclusion of the spin-orbit charge density and other correction terms is essential when aiming at extraction of subtle effects which become particularly visible in isotopic trends. It is also important when developing high-quality nuclear energy density functionals optimized using heterogeneous datasets involving absolute charge radii, differential charge radii, and charge form factor properties deduced from electron-scattering data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Mapping nanocrystal orientations via scanning Laue diffraction microscopy for multi-peak Bragg coherent diffraction imaging

The recent commissioning of a movable monochromator at the 34-ID-C endstation of the Advanced Photon Source has vastly simplified the collection of Bragg coherent diffraction imaging (BCDI) data from multiple Bragg peaks of sub-micrometre scale samples. Laue patterns arising from the scattering of a polychromatic beam by arbitrarily oriented nanocrystals permit their crystal orientations to be computed, which are then used for locating and collecting several non-co-linear Bragg reflections. The volumetric six-component strain tensor is then constructed by combining the projected displacement fields that are imaged using each of the measured reflections via iterative phase retrieval algorithms. Complications arise when the sample is heterogeneous in composition and/or when multiple grains of a given lattice structure are simultaneously illuminated by the polychromatic beam. Here, a workflow is established for orienting and mapping nanocrystals on a substrate of a different material using scanning Laue diffraction microscopy. The capabilities of the developed algorithms and procedures with both synthetic and experimental data are demonstrated. The robustness is verified by comparing experimental texture maps obtained with Laue diffraction microscopy at the beamline with maps obtained from electron back-scattering diffraction measurements on the same patch of gold nanocrystals. Such tools provide reliable indexing for both isolated and densely distributed nanocrystals, which are challenging to image in three dimensions with other techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Data for Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process- Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process- based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data- constrained representation of the sterile triploid Miscanthus (IL clone) within the process- based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam- derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field- measured biomass. Building on the calibrated operating state, parameter- response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post- calibration GPP R2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

Carbon↗

DOE OSTI2022

Tuscaloosa Marine Shale: Seal or Source? Petrophysical Comparative Study of Wells in SE Louisiana and SW Mississippi

The Tuscaloosa Marine Shale (TMS) is a versatile Late Cretaceous shale formation present in central and SE Louisiana and SW Mississippi, which drew attention because of the various roles played within the Tuscaloosa Group. In this paper, it is debated whether the Tuscaloosa Marine Shale can act as a source, reservoir, or seal all throughout the shale play or only in certain areas. Well log and core data from Adams County, Mississippi, are compared to data from East Feliciana Parish in Louisiana. Conclusions were drawn based on the results of well log analysis, X-ray Diffraction (XRD), porosity–permeability measurements, programmed pyrolysis, and fracture analysis. It was shown that the Tuscaloosa Marine Shale interval in SE Louisiana consists of important amounts of calcite, exhibits multiple natural fractures, has porosity values as high as 9.3%, and shows a TOC content of up to 2.8 wt%. On the other hand, samples from a well at the Cranfield field, MS, are characterized by considerably lower TOC values of around 0.88 wt%, porosities between 0.33% and 4%, and no serious fracturing. The formation demonstrates better reservoir and source potential in SE Louisiana and reliable CO2 sealing capacity in SW Mississippi. The analysis presented in this paper represents a holistic approach to the characterization of shale formations, is applicable to other plays around the world, and can be used as an integral part of CO2 sequestration or hydraulic fracturing programs.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2021

Geothermal Deep Direct-Use for Low-Carbon Heating: A Case Study at Cornell University

Cornell University is developing a geothermal deep direct-use system to provide reliable, low carbon, and cost-competitive heating for its main campus in Ithaca, New York. This geothermal system, also referred to as Earth Source Heat, would provide the majority of the campus heating load and represents a key pillar for the university to become carbon neutral by 2035. Over the last 10 years, numerous desktop, seismic, and geophysical studies have been undertaken at Cornell to characterize the local subsurface, predict long-term reservoir performance, design the integration into the existing district heating network, and estimate capital and levelized costs. Recently, the university received funding for drilling an observatory well to obtain in-situ subsurface data (e.g., reservoir temperatures, pressures, fluid chemistry, stress field), significantly lowering subsurface uncertainty and de-risking the project. Drilling of the test well is planned for fall 2021. This paper provides an overview and status of the project, including a summary of past studies and a description of the proposed well and planned testing activities.

Cornell University↗

DOE OSTI2021

Halo effective field theory analysis of one-neutron knockout reactions of Be 11 and C 15

Background: One-nucleon knockout reactions provide insightful information on the single-particle structure of nuclei. When applied to one-neutron halo nuclei, they are purely peripheral, suggesting that they could be properly modeled by describing the projectile within a halo effective field theory (halo-EFT). Purpose: We reanalyze the one-neutron knockout measurements of 11 Be and 15 C —both one-neutron halo nuclei—on beryllium at about 60 MeV/nucleon. We consider halo-EFT descriptions of these nuclei which already provide excellent agreement with breakup and transfer data. Method: Here we include a halo-EFT description of the projectile within an eikonal-based model of the reaction and compare its outcome to existing data. Results: Excellent agreement with experiment is found for both nuclei. The asymptotic normalization coefficients inferred from this comparison confirm predictions from ab initio nuclear-structure calculations and values deduced from transfer data. Conclusions: Halo-EFT can be reliably used to analyze one-neutron knockout reactions measured for halo nuclei and test predictions from state-of-the-art nuclear structure models on these experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. Here in this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for infield deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

42 ENGINEERING↗

DOE OSTI2021

Data Driven Approach to Analyzing The Impact of Power Plant Cycling on Air Preheater Degradation and Remaining Useful Life

Due to the increased penetration of renewable energy generation sources, and fluctuations of the oil and gas prices, modern coal burning power plants deal with increased variability in the demand for power generation. These varying demands result in their intermittent under-capacity operation (cycling). Periodical ramping down and back up to follow the daily power demands causes damages to the plant components reducing its operational life. In this paper we analyze the impact of cycling on a rotary Ljungstrom air preheater (APH) unit installed at a coal fire power plant in the US. An inefficient air preheater can significantly impact boiler performance. Due to the repeated boiler’s hot-cold start, the APH experiences fluctuating operating conditions that result in accelerated degradation mechanisms, such as dew-point corrosion, fouling/deposition plugging, and air heater leakage. The analysis in this paper utilizes field data related to APH basket replacement, and the number of cycles experienced by the boiler to model the life expectancy of the baskets. The data-driven model enables preventive maintenance strategies for the APH by predicting how long the APH baskets will last in a probabilistic sense. Furthermore, the analysis showed that an increase in cycling for a fixed operation time can reduce the APH basket remaining useful life by about 30%.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Microbial potentiometric sensor: A new approach to longstanding challenges

The underlying hypothesis of this study is that simple potentiometric measurements between sensing electrodes and a shared reference electrode - Microbial Potentiometric Sesnor (MPS) system - can be employed in a long-term, continuous mode of operation to resolve the spatial and temporal changes in environmental systems. To address the hypothesis, (1) a conceptual description of the MPS system and its postulated principle of operation are provided; (2) the MPS system performance is documented under controlled laboratory conditions; and (3) the capabilities of the MPS system are documented under quiescent and dynamic field condition. In a laboratory setting, the variability among different MPS signals was insignificant confirming the postulated high accuracy and reproducibility of the sensor performance. It also demonstrated statistically significant correlations with dissolved oxygen (DO) and oxidation-reduction potential (ORP) sensors, while showing capabilities of operating under anoxic and anaerobic conditions. Regardless of their locations in the model wetland system, three MPS sensors functioned without interruption and cleaning for a period >2 years, and thus demonstrating long-term durability of the MPS technology. In real batch-wastewater treatment facility, the deployed MPS system signals were able to describe the organic carbon trends and correlate with each treatment phase in a cycle. Data reproducibility and reliability exceeded the expectations better describing the carbon treatment levels than the DO and ORP sensors (p < 4.4 × 10 –162 vs phase adjusted p < 3.0 × 10 –58 ). While MPS signals correlate with specific parameters that describe the local process or environments, it is more prudent to employ both the magnitude and pattern of a composite signal like the MPS signal describe the change to reflect any shift in the local environment. Finally, when compared to a baseline pattern, this change in signal magnitude and pattern reveals important information that can be employed to tailor and optimize any condition or process which involves microorganisms.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Mapping valence electron distributions with multipole density formalism using 4D-STEM

Recent advancement in aberration correction and detector technology opened a door to various applications using 4D-STEM, which yields a diffraction pattern for each scanning position within a crystal unit-cell in scanning transmission electron microscopy (STEM) and generates incredible amounts of data in momentum space. Currently 4D-STEM analysis relies on the center-of-mass of the diffraction patterns in electric field and charge density mapping. It only derives the total projected charge density and is limited to phase objects, e.g. extremely thin samples. Here, we propose a new analytical method to accurately map aspherical valence electron distributions with atom-centered multipolar functions formalism using the whole 4D-STEM dataset. We demonstrate that, with the full dynamical calculations for various sample thicknesses, the method is sensitive not only to the miniscule charge transfer, but also to the atomic site symmetry and aspherical electron orbitals. The process of the refinement is much more robust and reliable than quantitative convergent beam electron diffraction.

4D-STEM↗

DOE OSTI2026

A Survey on the Expanding Scope and Interdisciplinary Opportunities for Processing-in-Memory Techniques

Processing-in-Memory (PIM) is emerging as a practical path to overcome the limitations of traditional von Neumann architectures. At its core, PIM systems implement computing primitives such as logic operations and multiply-accumulate acceleration through compute-in-memory, near-memory processing, or hybrid designs. The role of memory cells varies widely across technologies, acting as inputs, outputs, or analog accumulators through bit-lines and sense amplifiers. This diversity creates trade-offs in precision, bandwidth, latency, and programmability, making it difficult to build a unified understanding on the progress of the field. In this survey, we organize recent advances of PIM into three areas. First, we discuss the progress on the architectural optimizations of PIM and its integration with both DRAM and emerging non-volatile memories. Second, we examine how PIM is being used to accelerate key computing domains, including generative AI workloads and high-performance kernels, along with new approaches. Third, we highlight the growing adoption of PIM in computational sciences, where it is being applied to solve interdisciplinary problems such as genome analysis, mRNA quantification, mass spectrometry, quantum circuit simulation, wave modeling, and secure computation. Finally, we synthesize the major challenges that continue to slow PIM adoption, including manufacturing constraints, power delivery, thermal reliability, data consistency, runtime and memory-management coordination, and the difficulty of building portable software abstractions without sacrificing commercial viability. This work provides an updated, structured perspective on PIM’s potential across computing and computational sciences and the barriers that must be solved for it to reach its full impact.

Asifuzzaman, Kazi [Oak Ridge National Laboratory (↗

DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗

DOE OSTI2026

Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process‐Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process-based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data-constrained representation of the sterile triploid Miscanthus (IL clone) within the process-based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam-derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO 2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field-measured biomass. Building on the calibrated operating state, parameter-response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post-calibration GPP R 2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R 2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

ecosys↗

DOE OSTI2022

Analyzing rotational bands in odd-mass nuclei using effective field theory and Bayesian methods

We recently developed an Effective Field Theory (EFT) for rotational bands in odd-mass nuclei. Here we use EFT expressions to perform a Bayesian analysis of data on the rotational energy levels of 99 Tc, 155,157 Gd, 159 Dy, 167,169 Er, 167,169 Tm, 183 W, 235 U and 239 Pu. The error model in our Bayesian analysis includes both experimental and EFT truncation uncertainties. It also accounts for the fact that low-energy constants (LECs) at even and odd orders are expected to have different sizes. We use Markov Chain Monte Carlo (MCMC) sampling to explore the joint posterior of the EFT and error-model parameters and show both the LECs and the breakdown scale can be reliably determined. We extract the LECs up to fourth order in the EFT and find that, provided we correctly account for EFT truncation errors in our likelihood, results for lower-order LECs are stable as we go to higher orders. LEC results are also stable with respect to the addition of higher-energy data. We extract the expansion parameter for all the nuclei listed above and find a clear correlation between the extracted and the expected value of the inverse breakdown scale, W, based on the single-particle and vibrational energy scales. However, the W that actually determines the convergence of the EFT expansion is markedly smaller than would be naively expected based on those scales.

EFT↗