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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Automatic Multiple Experiment Simulation and Fitting (Ames-Fit)

AMES-Fit is a program used to automatically fit the multi-field solid-state NMR spectra of half-integer quadrupolar nuclei. Due to the high dimensional space, gradient algorithms have failed to address the fitting of such data, which is at present done manually. AMES-Fit diverges from these approaches by using an adaptive step size random search algorithm to fit the NMR spectra to consistently find the global best fit parameters.

Perras, Frederic↗

DOE OSTI2024

GADRAS-DRF Enhancements for Safeguards – Custom Peak Fitting to Enhance Model Fitting and Isotopics

One major software update was accomplished within the Gamma Detector Response and Analysis Software-Detector Response Function (GADRAS-DRF) package. This update allows users to adjust individual peak fits for use in subsequent analysis processes within GADRAS-DRF. A graphical user interface (GUI) was implemented so users can see the effect of their fit adjustments on the spectrum. This new feature will enhance the capability of the previously funded auto-enrichment and peak-based 1D model fit feature that was implemented in FY23 (Fiscal Year). Isotopic analysis was performed using M400 data obtained from uranium standards and the isotopic assessment is given with and without manual peak adjustments.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2020

K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data

Not provided.

Biotechnology & Applied Microbiology↗

DOE OSTI2025

Automated Resonance Fitting for Nuclear Data Evaluation

Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant manual effort and months of time from expert scientists. In this article, non-convex non-linear optimization methods are combined with concepts of inferential statistics to infer a resonance model from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

MITR DDE End Fitting Structural Rigidity Analysis

As a part of the design of the MITR Design Demonstration Element (DDE), a new design for the end fittings must be considered to meet specific experiment needs, e.g., compatibility with the geometrical restrictions of the DDE testing location (BR2 test reactor located in Mol, Belgium), and to allow for proper inspection of these test elements during the experiments. The design of the MITR DDE end fittings is substantially different from the design of the end fittings of the MITR LEU fuel elements, with the DDE end fittings considerably smaller in overall size and weight. Consequently, the stiffness of the end fittings as well as of the entire fuel element is affected. The end fittings in the MITR LEU fuel element are designed primarily for proper handling and flow distribution. The stiffness of the end fittings also constitutes a fraction of the total stiffness of the entire fuel element. It is debatable to what extent the end fittings provide the structural rigidity necessary for the fuel element to properly respond to potential loadings. It is presumed that the bulk of the overall stiffness of the element is derived from swaging multiple fuel plates into the side plates. However, the contribution of stiffness from various components of the MITR LEU fuel element and MITR DDE has not been determined so far. The primary objective of the work presented in this report was to assess the extent to which the stiffness of the end fitting in the MITR DDE contributes to the stiffness of the entire fuel element, and how it compares to the equivalent stiffness of the end fitting in the MITR LEU fuel element. Several simplified load cases were analyzed for that purpose using the geometries of the MITR LEU fuel element, MITR DDE, as well as that of the MITR DDE without the top end fitting.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

Abstract The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness and genotype-by-environment interactions hinder our understanding of the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different genetic variant types for a Saccharomyces cerevisiae population. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation showed that different variant types identified distinct gene sets associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to predictions were both isolate- and environment-dependent, pointing to gene-by-gene and gene-by-environment interactions. Furthermore, models uncovered experimentally validated and novel candidate genetic interactions for a well-characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different genetic variant types and offer novel targets for future functional analysis.

DNA copy number variations↗

DOE OSTI2024

The CXSFIT spectral fitting code: Past, present and future

Magnetically confined plasma experiments generate a wealth of spectroscopic data. The first step toward extracting physical parameters is to fit a spectral model to the often complex spectra. The CXSFIT (Charge eXchange Spectroscopy FITting) spectral fitting code was originally developed for fitting charge exchange spectra on JET from the late 1980s onward and has been further developed over decades to keep up with the needs of the users. The primary use is to efficiently fit a large number of spectra with many constrained Gaussian spectral lines of which the physical parameters can be coupled in a user-friendly manner. More recent additions to the code include time-dependent couplings between parameters, flexible background subtraction, and a non-linear coupling scheme between fit parameters. The latter was a pre-requisite for implementing Zeeman and motional Stark effect multiplets in the library of spectral features. The ability to save and replay “fit recipes,” even when multiple iterations are required, has ensured the traceability of the results and is one of the keys to the longevity and success of the code. The code is also in use on other tokamaks (AUG, ST-40) and to fit data from other spectroscopic diagnostics on JET. In this paper, we document the current capabilities and philosophy behind the structure of the code, including some of the algorithms used to calculate spectral features numerically efficiently. We also provide an outline of how CXSFIT could be transferred into a framework that would be able to meet the spectral fitting requirements of future devices, such as ITER.

Delabie, Ephrem G.↗

DOE OSTI2024

Automatic fitting of multiple-field solid-state NMR spectra

The NMR lineshapes produced by half-integer quadrupolar nuclei are sensitive to 11 distinct fit parameters per inequivalent site. To date, automatic fitting routines have failed to replace manual parameter insertion and evaluation due to the importance of local minima and the need for fitting multiple-field magic-angle spinning (MAS) and static spectra simultaneously. Herein we introduce a new tool, AMES-Fit (Automatic Multiple Experiment Simulation and Fitting), to automatically find the global best-fit simulation parameters for a series of multiple-field NMR lineshapes. AMES-Fit uses an adaptive step size random search algorithm to dynamically probe parameter space and requires minimal human input. Importantly, the best fits are obtained in a few minutes of computation time that would otherwise have required several person-hours of work. The program is freely available and open-source.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Molecular-field-theory fits to magnetic susceptibilities of antiferromagnetic GdCu 2 Si 2 , CuO, LiCrO 2 , and α-CaCr 2 O 4 single crystals below their Néel temperatures

A recently-developed molecular field theory (MFT) has been used to fit single-crystal magnetic susceptibility χ versus temperature T data below the respective antiferromagnetic ordering temperature T N for a variety of collinear and coplanar noncollinear Heisenberg antiferromagnets. The spins in the system are assumed to interact by Heisenberg exchange and to be identical and crystallographically equivalent. The fitting parameters for χ(T ) of collinear antiferromagnets are measurable quantities: the Weiss temperature θ p in the Curie-Weiss law, TN, χ(T N ), and the spin S. For coplanar noncollinear helix and cycloid structures, an additional fitting parameter is the turn angle between layers of ferromagnetically-aligned spins. Here MFT fits to anisotropic χ(T) data from the literature for single crystals of the collinear antiferromagnets GdCu 2 Si 2 and CuO and the noncollinear antiferromagnets LiCrO 2 with a 120° cycloidal structure and α-CaCr 2 O 4 with a 120° helical structure below their respective N´eel temperatures are presented. The MFT fit to the anisotropic χ(T ≤ T N ) data for CuO is poor, whereas the fits to the data for GdCu 2 Si 2 , LiCrO 2 , and α-CaCr 2 O 4 are quite good. The poor fit for CuO is attributed to the influence of strong quantum fluctuations associated with the small Cu spin and the quasi-one-dimensional magnetism that are not taken into account by the MFT. The magnetic contribution to the zero-field heat capacity of the collinear antiferromagnet GdNiGe 3 at T ≤ T N is also fitted by the MFT.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Framework for X-ray mirror surface shape fitting

For accurate characterization of grazing-incidence X-ray mirrors, we present a comprehensive framework to fit measured surface shapes (either slope or height) of X-ray mirrors used in synchrotron radiation and free-electron laser facilities. We summarize the closed-form expressions of some typical surface shapes of X-ray mirrors including elliptic cylinders, hyperbolic cylinders, ellipsoids, hyperboloids, and diaboloids. This framework is composed of four layers: definition of standard shapes with closed-form expressions, generation of theoretical surface with pose parameters (six degrees of freedom defining an object's position and orientation relative to a coordinate system), parameter optimization with the ability to select which parameters are fit and which are held constant, and the development of user-friendly fitting function wrappers for particular fitting tasks. A few practical fitting examples are demonstrated to verify the effectiveness of the proposed fitting framework. We discuss the physical meanings of the fitting parameters, and provide several examples using the elliptic cylinder and ellipsoid shapes to highlight some features of the framework. Moreover, we provide the presented framework as open-source codes (MATLAB and Python codes available at https://github.com/nsls2omf/xmf) to the community to encourage academic collaboration and further improvements.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis

Multi-dimensional spectral-imaging is a mainstay of the scanning probe and electron microscopies, micro-Raman, and various forms of chemical imaging. In many cases, individual spectra can be fit to a specific functional form, with the model parameter maps, providing direct insight into material properties. Since spectra are often acquired across a spatial grid of points, spatially adjacent spectra are likely to be similar to one another; yet, this fact is almost never used when considering parameter estimation for functional fits. On datasets tried here, we show that by utilizing proximal information, whether it be in the spatial or spectral domains, it is possible to improve the reliability and increase the speed of such functional fits by ~2-3x, as compared to random priors. We explore and compare three distinct new methods: (1) spatially averaging neighborhood spectra, and propagating priors based on functional fits to the averaged case, (2) hierarchical clustering-based methods where spectra are grouped hierarchically based on response, with the priors propagated progressively down the hierarchy, and (3) regular clustering without hierarchical methods with priors propagated from fits to cluster means. Our results highlight that utilizing spatial and spectral neighborhood information is often critical for accurate parameter estimation in noisy environments, which we show for ferroelectric hysteresis loops acquired on a prototypical PbTiO3 thin film with piezoresponse spectroscopy. This method is general and applicable to any spatially measured spectra where functional forms are available. Examples include exploring the superconducting gap with tunneling spectroscopy, using the Dynes formula, or current-voltage curve fits in conductive atomic force microscopy mapping. Here we explore the problem for ferroelectric hysteresis, which, given its large parameter space, constitutes a more difficult task than, for example, fitting current-voltage curves with a Schottky emission formula.

42 ENGINEERING↗

DOE OSTI2023

Insufficient reporting of x-ray photoelectron spectroscopy instrumental and peak fitting parameters (metadata) in the scientific literature

This study was motivated by earlier observations. It is a systematic examination of the adequacy of reporting of information (metadata) necessary to understand x-ray photoelectron spectroscopy (XPS) data collection and data analysis in the scientific literature. The information for this study was obtained from papers published in three high-quality journals over a six-month period in 2019 and throughout 2021. Each paper was evaluated to determine whether the authors had reported (percentages of the papers properly providing the information are given in parentheses) the spectrometer (66%), fitting software (15%), x-ray source (40%), pass energy (10%), spot size (5%), synthetic peak shapes in fits (10%), backgrounds in fits (10%), whether the XPS data are shown in the main body of the paper or in the supporting information (or both), and whether fitted or unfitted spectra were shown (80% of published spectra are fit). The Shirley background is the most widely used background in XPS peak fitting. The Al Kα source is the most widely used x-ray source for XPS data collection. CASAXPS is the most widely used fitting program for XPS data analysis. Further, there is good agreement between the results gathered during the two years of our survey. There are some hints the situation may be improving. This study also provides a list of the information/parameters that should be reported when XPS is performed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Measurements of three-flavor neutrino oscillations from a PISCES two-detector fit to the NOvA Experiment data

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA s primary physics goals are to measure the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ with high precision, determine the neutrino mass hierarchy, and constrain the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, a strategy that suffers from poor scalability as the number of uncertainties becomes larger. This work introduces PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES utilizes a nested minimization in which optimal systematic pulls are first computed using the covariance matrix in an inner minimization step, then the oscillation parameters are profiled over in the outer minimization. PISCES also uses a Poisson Likelihood term, making it ideal for the inclusion of low-statistic samples in the fits. PISCES is a flexible framework that also supports complex fits, such as a joint Near and Far detector fit. In the standard NOvA analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data indirectly constrain the FD prediction via a ratio method. PISCES, on the other hand, enables a simultaneous ND+FD fit, allowing the high-statistics ND data to directly constrain systematic uncertainties across all samples. This thesis presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, details its implementation, and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method using the full NOvA 10-year data set. The results demonstrate that PISCES can successfully fit NOvA data while incorporating the constraints from the ND detectors consistently, using physically motivated systematic uncertainties to account for data/MC discrepancies.

Rajaoalisoa, Miriama [Cincinnati U.]↗

DOE OSTI2022

High–throughput measurement of plant fitness traits with an object detection method using Faster R–CNN

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population’s mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

59 BASIC BIOLOGICAL SCIENCES↗