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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Formatting and V&V of Consistent 238,240−24 2Pu $\overline{v}_p$ Evaluated Mean Values and Covariances

This report is in answer to the Nuclear Criticality Safety Program FY24 quarter 4 milestone that requires: “Format and V&V nu-bar means and covariances” for 238,240-242 Pu average prompt fission neutron multiplicities, $\overline{ν}$ p , that were obtained by a consistent evaluation leveraging the fission-event generator CGMF and a detailed uncertainty quantification of experimental data. It is described how nuclear data mean values and covariances were formatted using ENDFtk. Implementing the new 238,240-242 Pu $\overline{ν}$ p into the ENDF/B-VIII.1β 4 library leads to only small overall changes in criticality values of the Jezebel, Dirty Jezebel, Jupiter-001, Jupiter-002, EUCLID 3x2 and EUCLID 8x1 critical assemblies. Simulated k eff uncertainties due to $\overline{ν}$ p covariances change only little if cross-isotope covariances are considered or not for those assemblies with low percentage content of minor Pu isotopes. However, for the Dirty Jezebel critical assembly, that has a sizeable 240 Pu and non-negligible 241 Pu content, the simulated k eff uncertainties due to considering or neglecting cross-isotope $\overline{ν}$ p covariances is 443 versus 374 pcm.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Utqiagvik (Barrow), Alaska, 2012 - 2019

Measurements began in September 2012 and are generally made throughout the snow-free growing season starting from April (pre-thawing) until November (freeze-up) when the instrumentation is taken down for maintenance and instrument calibration. CO2, CH4 and energy fluxes using the Eddy Covariance (EC) technique (Baldocchi 2003) and meteorological measurements are reported as 30-minute averages. The tower site is registered with AmeriFlux as US-NGB. See http://ameriflux.lbl.gov/sites/siteinfo/US-NGB for more information. Reported data include: (1) additional/extra meteorological and eddy covariance CO2, CH4 and energy flux data, (2) binned spectra for the three wind components, the sonic temperature and gas densities together with the binned cospectra for covariances of w (vertical wind component) and gas densities, (3) binned ogives (cumulative (co)spectra) for the three wind components, the sonic temperature and gas densities, and (4) tower site footprint matrices. Meteorological and eddy covariance CO2, CH4 and energy flux data (AmeriFlux BASE data product) are available from AmeriFlux https://doi.org/10.17190/AMF/1436326. The tower site is adjacent to the NGEE Arctic Tram system with several complementary time-series measurements https://doi.org/10.5440/1558782.This dataset supersedes the previously released dataset "Eddy-Covariance and auxiliary measurements, NGEE-Barrow, 2012-2013" DOI:10.5440/1124200 as this new product includes data for the longer time period processed using consistent methods for the entire record. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) near Utqiagvik (Barrow), Alaska and 2) multiple areas in the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

UDU/T/ covariance factorization for Kalman filtering

There has been strong motivation to produce numerically stable formulations of the Kalman filter algorithms because it has long been known that the original discrete-time Kalman formulas are numerically unreliable. Numerical instability can be avoided by propagating certain factors of the estimate error covariance matrix rather than the covariance matrix itself. This paper documents filter algorithms that correspond to the covariance factorization P = UDU(T), where U is a unit upper triangular matrix and D is diagonal. Emphasis is on computational efficiency and numerical stability, since these properties are of key importance in real-time filter applications. The history of square-root and U-D covariance filters is reviewed. Simple examples are given to illustrate the numerical inadequacy of the Kalman covariance filter algorithms; these examples show how factorization techniques can give improved computational reliability.

Thornton, C. L.↗
NASA NTRS1993

Galilean covariant harmonic oscillator

A Galilean covariant approach to classical mechanics of a single particle is described. Within the proposed formalism, all non-covariant force laws defining acting forces which become to be defined covariantly by some differential equations are rejected. Such an approach leads out of the standard classical mechanics and gives an example of non-Newtonian mechanics. It is shown that the exactly solvable linear system of differential equations defining forces contains the Galilean covariant description of harmonic oscillator as its particular case. Additionally, it is demonstrated that in Galilean covariant classical mechanics the validity of the second Newton law of dynamics implies the Hooke law and vice versa. It is shown that the kinetic and total energies transform differently with respect to the Galilean transformations.

Horzela, Andrzej↗
NASA NTRS1995

On-line estimation of error covariance parameters for atmospheric data assimilation

A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.

Dee, Dick P.↗
NASA NTRS2007

Comparing Consider-Covariance Analysis with Sigma-Point Consider Filter and Linear-Theory Consider Filter Formulations

Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to nonlinear sequential consider covariance analysis, i.e. in the presence of nonlinear dynamics and nonlinear measurements. A simple SPCF for orbit determination, exemplifying an algorithm hosted in the guidance, navigation and control (GN&C) computer processor of a hypothetical robotic spacecraft, was implemented, and compared with an identically-parameterized (standard) extended, consider-parameterized Kalman filter. The onboard filtering scenario examined is a hypothetical spacecraft orbit about a small natural body with imperfectly-known mass. The formulations, relative complexities, and performances of the filters are compared and discussed.

Lisano, Michael E.↗
NASA NTRS2000

Adjoints and Low-rank Covariance Representation

Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.

Tippett, Michael K.↗
NASA NTRS2015

Covariance Between Arctic Sea Ice and Clouds Within Atmospheric State Regimes at the Satellite Footprint Level

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and mid-tropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

Taylor, Patrick C.↗
NASA NTRSDate not supplied

Demonstration of Linear Covariance Analysis Techniques to Evaluate Entry Descent and Landing Guidance Algorithms, Vehicle Configurations, Analysis Techniques, and Trajectory Profiles

Linear covariance analysis techniques have been previously developed to analyze closed-loop entry, descent, and landing (EDL) scenarios and the initial validation efforts are under-way confirming the generated GN&C system performance results. Given both the theoretical foundation and previous conceptual demonstration, this work begins to flex the potential of linear covariance analysis for atmospheric flight and highlight its versatility and reliability by evaluating multiple entry guidance algorithms, vehicle configurations, trajectory profiles, environment conditions, and analysis techniques for a variety of trade studies. To demonstrate the benefit linear covariance analysis can provide in producing rapid yet accurate performance data, two entry profiles are adopted including the NASA Mars Science Laboratory (MSL) and Exploration Flight Test-1 (EFT-1) while utilizing two different guidance algorithms, the Apollo Final Phase (AFP) and the Fully Numeric Predictor-Corrector Entry Guidance (FNPEG) with different navigation sensor suites in a 6 degree-of-freedom (6-DOF) simulation environment. Results are shown using both linear covariance and Monte Carlo analysis techniques to high-light the consistency between the two methodologies and continue the validation maturation of linear covariance analysis for entry, descent, and landing.

EDL↗
DOE OSTI2023

Robust inference of ecosystem soil water stress from eddy covariance data

Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. Here, we develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site’s soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Study of the Covariance Data in ENDF/B VIII.0 for Low Z Isotopes

Thirty group covariance data have been produced from ENDF/B VIII.0 MF 33 for H1, H2, He4, Li6, Li7, Be9, B10, B11, C12, C13, N15, O16, the JENDL O16 and F19. Multi-group cross section covariance data was produced by NJOY routine ERRORR. Negative eigenvalues were found only for F19, but the covariance constraints required for the condition of the sum of partial cross sections being equal to the resultant total cross section were found to be incorrect for H2, Li6, B10, C12, C13, and O16. An easy correction is suggested for Li6 and C13. In closing, an expansion of the ENDF-6 formats manual is suggested for the MF 33 chapter and a new proposed covariance normalization scheme is proposed.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Impact of nuclear data covariance libraries on uncertainty quantification of sodium cooled fast reactor simulation

Uncertainty quantification in core modelling to assess reactor performance is a regular practice to identify design safety limits. Best-estimate plus uncertainty (BEPU) methods are commonly used to propagate uncertainties to core responses from input parameters, characterized by variance-covariance libraries. This study evaluates the impact of different covariance libraries on the uncertainty associated with core safety parameters and their propagation through reactor transient modelling for a metallic core (ABR-1000). The COMAC-1.0, COMMARA-2.0, and COMMARA-2.1 covariance libraries are used to propagate nuclear data uncertainties to neutron feedback coefficients using the Generalized Perturbation Theory, then through transient safety analyses to establish the confidence interval for safety performance of the reactor using stochastic sampling. The considered transients include Unprotected Transient Over Power and Unprotected Loss Of Flow. Most influential nuclide and reaction pairs in different variance-covariance libraries are identified and their impact on the uncertainty of safety parameters are evaluated. Significant differences were observed between uncertainties from COMAC compared to COMMARA libraries. Uncertainties from COMMARA libraries were consistently larger than COMAC for all cases. For structure, coolant density feedback coefficients and control rod worth, total nuclear data uncertainties were approximately 50% larger for COMMARA than COMAC. Differences were also observed in identification of top contributors of uncertainty in terms of reaction-cross sections. For instance, largest contribution to total uncertainty of radial expansion coefficient from COMMARA-2.0 and 2.1 originates from Na{sup 23} P1 Elastic cross section (0.81%) but from Pu{sup 239} Fission (0.53%) with COM C-1.0. Such differences are evaluated in this research for all quantities of interest. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
NASA NTRSDate not supplied

Generalized Augmented-State Covariance Analysis for Spaceflight

The use of linear covariance analysis techniques, also known as LinCov, has been used extensively for more than a half century for spaceflight applications. Originally, its primary purpose was to facilitate navigation analysis. For many past and current applications, the specific implementations only support navigation studies still. When the concept of an augmented-state linear covariance analysis approach was initially introduced that allowed for both navigation and trajectory dispersion analysis, the enhancement was motivated and primarily utilized to support navigation filter tuning and error budget analysis. Relatively few utilize this alternate augmented-state formulation of LinCov due to its additional complexity. The untapped potential of the augmented-state linear covariance analysis technique slowly unfolded in the past two-decades as its capability to rapidly and reliably capture the integrated closed-loop guidance, navigation, and control (GN&C) system performance became more apparent. Even with this dual purpose of generating insights to both navigation errors along with trajectory and delta-v dispersions, the core theoretical development had a heavy emphasis on the impacts of the navigation system and largely neglected the details of the actual guidance, targeting, and control systems. This paper extends the navigation-centric theoretical development by formulating a generalized augmented-state covariance analysis (GAUSCOV) technique that allows for the intricacies of a variety of targeting and control strategies along with ground planning and mission operations to be more formally included in assessing the impacts to spaceflight GN&C system performance.

Linear Covariance Analysis↗
DOE OSTI2024

Hilbert series for covariants and their applications to minimal flavor violation

We elaborate how to apply the Hilbert series method to enumerating group covariants, which transform under any given representation, including but going beyond group invariants. Mathematically, group covariants form a module over the ring of the invariants. The number of independent covariants is given by the rank of the module, which can be computed by taking a ratio of two Hilbert series. In many cases, the rank equals the dimension of the group covariant representation. When this happens, we say that there is a rank saturation. We apply this technology to revisit the hypothesis of Minimal Flavor Violation in constructing Effective Field Theories beyond the Standard Model. We find that rank saturation is guaranteed in this case, leading to the important consequence that the MFV symmetry principle does not impose any restriction on the EFT, i.e. MFV SMEFT = SMEFT, in the absence of additional assumptions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Covariant color-kinematics duality

We show that color-kinematics duality is a manifest property of the equations of motion governing currents and field strengths. For the nonlinear sigma model (NLSM), this insight enables an implementation of the double copy at the level of fields, as well as an explicit construction of the kinematic algebra and associated kinematic current. As a byproduct, we also derive new formulations of the special Galileon (SG) and Born-Infeld (BI) theory. For Yang-Mills (YM) theory, this same approach reveals a novel structure — covariant color-kinematics duality — whose only difference from the conventional duality is that 1/$\square$ is replaced with covariant 1/D 2 . Remarkably, this structure implies that YM theory is itself the covariant double copy of gauged biadjoint scalar (GBAS) theory and an F 3 theory of field strengths encoding a corresponding kinematic algebra and current. Directly applying the double copy to equations of motion, we derive general relativity (GR) from the product of Einstein-YM and F 3 theory. This exercise reveals a trivial variant of the classical double copy that recasts any solution of GR as a solution of YM theory in a curved background. Covariant color-kinematics duality also implies a new decomposition of tree-level amplitudes in YM theory into those of GBAS theory. Using this representation we derive a closed-form, analytic expression for all BCJ numerators in YM theory and the NLSM for any number of particles in any spacetime dimension. By virtue of the double copy, this constitutes an explicit formula for all tree-level scattering amplitudes in YM, GR, NLSM, SG, and BI.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Applying a Template of Expected Uncertainties to Updating 239 Pu(n,f) Cross-section Covariances in the Neutron Data Standards Database

Templates of uncertainties expected in specific measurement types were recently developed. One aim of these templates is to help evaluators in identifying (1) missing or suspiciously low uncertainties and (2) missing correlations between uncertainties of the same and different experiments, when estimating covariances for experimental data employed in their evaluations. These templates also provide realistic estimates of standard deviations and correlations for a particular uncertainty source and measurement type that can be used by evaluators in situations where they are not supplied by the experimenters. This information allows for a more comprehensive uncertainty analysis across all measurements considered in an evaluation and, thus, more realistic evaluated covariances. Here, in this work, we extend a template that is applicable to uncertainties expected in neutron-induced fission, (n,f), cross-section measurements. It is applied to improving covariances of 239 Pu(n,f) cross-section measurements in the database underlying the Neutron Data Standards evaluations. This particular example was chosen since this evaluation is primarily based on experimental information. Also, some uncertainties of individual 239 Pu(n,f) cross-section experiments in this database were suspected to be underestimated. The evaluated uncertainties obtained after updating the covariances in the database by means of the template indeed do increase compared to their original values. Even more importantly, the evaluated mean values change noticeably. These modified cross sections impact application calculations significantly, as is demonstrated by employing them in simulations of the effective neutron multiplication factor for a few selected critical assemblies. However, this updated evaluated 239 Pu(n,f) cross section should not be interpreted as the final one that should replace values of the current Neutron Data Standards project. Evaluations for the Neutron Data Standards of the 239 Pu(n,f) cross section must be linked to many other observables included in the associated database, most notably to cross sections for 235 U(n,f), but also to those for 10 B(n, α ), 6 Li(n,t), 238 U(n,f), and 238 U(n, γ ), because of included measurements of the 239 Pu(n,f) cross section that appear as ratios to these reactions. Some of these other reactions are correlated to further observables in the database. Hence, updating uncertainties of data sets of any of these observables can potentially impact the 239 Pu(n,f) cross section. Uncertainties for all measurements of these linked physical observables have to be updated before a comprehensive evaluation of the 239 Pu(n,f) cross section and its corresponding uncertainties can be provided.

239Pu Neutron-induced fission cross-section↗