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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

“Soft” muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb -1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Tools for unbinned unfolding

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. Here, in order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Training toward significance with the decorrelated event classifier transformer neural network

Experimental particle physics uses machine learning for many tasks, where one application is to classify signal and background events. This classification can be used to bin an analysis region to enhance the expected significance for a mass resonance search. In natural language processing, one of the leading neural network architectures is the transformer. In this work, an event classifier transformer is proposed to bin an analysis region, in which the network is trained with special techniques. The techniques developed here can enhance the significance and reduce the correlation between the network’s output and the reconstructed mass. It is found that this trained network can perform better than boosted decision trees and feed-forward networks. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Search for charged-lepton flavor violation in the production and decay of top quarks using trilepton final states in proton-proton collisions at $\sqrt{s}$ =13 TeV

A search is performed for charged-lepton flavor violating processes in top quark (𝑡) production and decay. The data were collected by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 138 fb −1 . The selected events are required to contain one opposite-sign electron-muon pair, a third charged lepton (electron or muon), and at least one jet of which no more than one is associated with a bottom quark. Boosted decision trees are used to distinguish signal from background, exploiting differences in the kinematics of the final states particles. The data are consistent with the standard model expectation. Upper limits at 95% confidence level are placed in the context of effective field theory on the Wilson coefficients, which range between 0.024–0.424 TeV −2 depending on the flavor of the associated light quark and the Lorentz structure of the interaction. These limits are converted to upper limits on branching fractions involving up (charm) quarks, 𝑡 → 𝑒⁢𝜇⁢𝑢 (𝑡 → 𝑒⁢𝜇⁢𝑐), of 0.032⁢(0.498) × 10 −6 , 0.022⁢(0.369) × 10 −6 , and 0.012⁢(0.216) × 10 −6 for tensorlike, vectorlike, and scalarlike interactions, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Low Activity Tritium Detection in CCDs Using Deep Learning Techniques

Here, this study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonproliferation, and environmental monitoring. We employ a dual approach utilizing both measured CCD data and detailed Geant4 simulations. Our analysis compares classical techniques with advanced deep learning methods, including convolutional neural networks (CNNs), autoencoders trained exclusively on tritium data, and preliminary studies on boosted decision trees (BDTs). The CNN, trained on mixed signal/background datasets, demonstrates superior classification performance, while the autoencoder shows the potential of unsupervised, background-agnostic strategies when background characteristics are poorly defined. These results highlight the excellent sensitivity achievable thanks to the background rejection made possible by information-rich CCD data, paving the way for improved portable tritium monitoring.

Autoencoder↗
DOE OSTI2025

Reconstruction and identification of pairs of collimated τ-leptons decaying hadronically using s=13 TeV pp collision data with the ATLAS detector

This paper describes an algorithm for reconstructing and identifying a highly collimated hadronically decaying τ$$\tau $$-lepton pair with low transverse momentum. When two τ$$\tau $$-leptons are highly collimated, their visible decay products might overlap, degrading the reconstruction performance for each of the τ$$\tau $$-leptons. A dedicated treatment attempting to tag the τ$$\tau $$-lepton pair as a single object is required. The reconstruction algorithm is based on a large radius jet and its associated two leading subjets, and the identification uses a boosted decision tree to discriminate between signatures from τ+τ-$$\tau ^+\tau ^-$$ systems and those arising from QCD jets. The efficiency of the identification algorithm is measured in Zγ$$Z\gamma $$ events using proton–proton collision data at s=13$$\sqrt{s}=13$$ TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139fb-1$$139\,\text{ fb}^{-1}$$. The resulting data-to-simulation scale factors are close to unity with uncertainties ranging from 26 to 37%.

Aad, G↗
DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2024

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Aghdam, Rosa↗
DOE OSTI2024

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Abstract Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Mitigating Algorithmic Bias in Cancer Site Classification Models

Purpose Integrating artificial intelligence in cancer diagnostics has improved tumor classification beyond rule-based systems. Despite these advancements, these models may still encode demographic biases. We conducted a large-scale, applied bias-probing study of a deep learning–based cancer site classifier to quantify race information encoded in document embeddings. We then evaluated how performance changes when race-correlated embedding dimensions are removed in a post-training sensitivity analysis. Methods The cancer site classifier was trained using 3.5 million electronic cancer pathology reports from six of the National Cancer Institute's SEER registries. We trained a hierarchical self-attention network to generate 400-dimensional document embeddings. These embeddings were used to train two downstream, gradient-boosted decision tree classifiers: one to classify the cancer sites and another to predict racial categories. We identified overlapping features by intersecting the top 50 feature-importance rankings from the site and race models and computed their cumulative feature importance in each model. As a post hoc sensitivity analysis, we progressively pruned these overlapping dimensions, retrained the site model, and compared overall macro-F1 and accuracy, race-stratified macro-F1, and group fairness metrics on the basis of demographic parity and equalized odds before and after pruning. Results The analysis revealed minimal feature overlap between the cancer site and race prediction models, and the cumulative importance scores indicated a negligible influence of racial information on clinical predictions. Post-training pruning of overlapping features did not compromise the models' diagnostic accuracy, with a 0.07% loss in accuracy. Conclusion Our findings demonstrate that HiSAN-generated embeddings from SEER data can be used effectively in cancer site classification without significant demographic bias influencing the outcomes. Post-training pruning therefore functions as a practical audit and sensitivity check.

Shivanna, Abhishek [ORNL] (ORCID:0009000665228593)↗
DOE OSTI2025

Simultaneous prediction of structural properties in epitaxially–grown GaN with quantum and conventional multi–output learning algorithms

Hundreds of GaN thin film crystal plasma–assisted molecular beam epitaxy synthesis experiment records spanning two decades were organized into a dataset correlating the growth experiment design parameters with discrete, binary determinations of crystallinity and surface morphology. Conventional data science techniques as well as both quantum and classical multi–output supervised machine learning algorithms were implemented to investigate the relationships between the operating parameter data and the structural figures of merit. Correlation coefficients, decision tree nodes, p–values, and SHAP values all support substrate temperature and gallium effusion cell conditions as being statistically significant for simultaneously influencing GaN crystallinity and surface morphology. Here, a conventional deep neural network learned best from the data, followed by a quantum–classical hybrid gradient boosting algorithm. When combined with calculations of uncertainty intervals based on VennAbers predictors, machine learning predictions of both structural properties show good agreement with results reported in published experimental literature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Track vs Shower Discrimination in the Event Reconstruction of the ICARUS Experiment

The ICARUS collaboration has employed the 760-ton T600 liquid argon TPC detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. The detector commissioning phase lasted until June 2022, then ICARUS moved to data taking for neutrino oscillation physics collecting events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis. The initial experiment goals are to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment, perform measurements of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. Then, ICARUS will jointly search for evidence of sterile neutrinos with the Short-Baseline Near Detector (SBND). In this contribution, we discuss recent changes to the standard TPC event reconstruction that uses Pandora, a pattern recognition software common to liquid argon-based detectors. In particular, we performed a new training of the Boosted Decision Tree (BDT) employed to separate track-like and shower-like reconstructed particles using Monte Carlo simulations of neutrino events from BNB in ICARUS. We compare the discrimination capabilities of the old and new BDT training and discuss further improvements of this algorithm.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Logical Approach to Tritium Vacuum Pump Selection

Introduction Vacuum pumps are essential to the operation of tritium purification and recovery processes. However, not all vacuum pumps sold meet the requirements for tritium service. Manufacturers routinely do not fully appreciate the hazards associated with tritium, or the complex materials interactions. Therefore, a decision tree was established detailing which pump types, and certain manufacturers, are either fully compatible, limited use, or to avoid at all costs to establish a more structured and uniform approach to tritium vacuum pumps.

ANGELETTE, LUCAS↗
DOE OSTI2024

Geometric Measures of Trustworthiness for Machine Learning Predictions

his report details the findings from the research and investigation of Geometric Measures of Trustworthiness for Machine Learning Predictions. We explored the trustworthiness of machine learning (ML) models’ predictions using geometric measures to quantify the similarity of a query point with the training data. Predictive uncertainty in ML can originate from at least three sources: (1) Model uncertainty, which represents the uncertainty in model form (e.g. decision tree, vs neural network) and estimating the model parameters from the training data, (2) Data uncertainty, which represents the natural complexities of the data such as class overlap and inherent noise, and (3) Distributional uncertainty, which represents the mismatch between the training and operational distributions. The proposed measures focus on measuring and explaining the data and distributional uncertainties by measuring the relationships of operational data with the training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Operational Forecasting of Induced Seismicity (CRADA Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC ("LLNS"), as manager and operator of Lawrence Livermore National Laboratory ("LLNL"), The Regents of the University of California, as manager and operator of Lawrence Berkeley National Laboratory (Collectively, Contractors) and Nanometrics, Inc. ("Participant"), to develop a toolkit called "Operational Forecasting of Induced Seismicity (ORION)" that includes a decision tree method for operational forecasting of induced seismicity rates related to fluid disposal operations.

58 GEOSCIENCES↗
DOE OSTI2025

Connectivity Troubleshooting Guide for Advanced Electricity Meters

This guide is designed to walk users through a troubleshooting process for disconnected advanced electricity meters. The guide centers on two connectivity troubleshooting decision trees related to power issues and network connectivity issues, respectively. Additionally, it contains additional background information such as definitions, diagrams, and checklists that can help a user prepare to troubleshoot meter connectivity issues.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Exploring the Whole Set of Accurate Sparse Interpretable Models

In data science applications, there are often many models that fit the data well. This phenomenon was called the Rashomon Effect by Leo Breiman. The set of good models is called the Rashomon Set, and the goal of this project is to locate, store, and study the Rashomon sets for classes of interpretable models, including decision trees and generalized additive models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Baseline Hypothetical Facility for the Production of 131 I and 99 Mo using Activation Targets

This report describes a hypothetical facility for production of medical radioisotopes via activation under the Proliferation Resistance and Optimization (PRO-X) program. The facility uses neutron activation of non-special nuclear material (SNM) to produce the medical isotopes 131 I and 99 Mo at a throughput of 60 Ci/week of 131 I and 5 Ci/week of 99 Mo. The hypothetical design was carried out using a 10 MWt research reactor. The precursors used for the activation process were TeO2 for 131 I and MoO 3 for 99 Mo. The processes are performed in 3 hot cells used for target receipt, extraction, purification low specific activity (LSA) generator introduction, and packaging. A fourth hotcell is used for waste processing. The hot cell processing area takes up a footprint of 15.4 m 2 with the total footprint of the facility, including space for administrative offices, non-rad labs, quality assurance, and radiation buffer areas set at 763 m 2 . Waste is produced at a weekly rate of 257.8 g low activity solid waste and 8032.7 mL of low activity liquid waste, 8032 mL of which is water. This baseline hypothetical facility for production of medical isotopes via activation was then compared and contrasted to the hypothetical facility for production of medical isotopes via fission products to show the differences in approach for the two production modes. The two production modes had several highlighted differences including the overall facility and hot cell layout, the type and amount of waste produced by the respective facilities, and economic factors impacting production mode. Finally, a decision tree for which production mode might be more beneficial for an entrant into medical isotope production was developed based on the differences examined and the desired output of medical isotopes desired by the entrant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗