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DOE OSTI2023

Microwave radiometer, sun-photometer and GNSS multi-comparison of integrated water vapor in Southwestern Europe

This work presents a comparison of integrated water vapor (IWV) data recorded from microwave radiometer (MWR) and sun-photometer (SP) using global navigation satellite system (GNSS) IWV as reference in five mid-latitude sites of Portugal and Spain (2003–2021). A very high correlation is obtained for both instruments (R2 between 0.94 and 0.98), although, while MWR shows a wet bias, SP exhibits a dry one. In addition, a dependence of mean bias error (MBE) and standard deviation (SD) on IWV has been observed, increasing (larger discrepancies) both indices as IWV increases. The solar zenith angle (SZA) dependence is also studied, finding slightly larger discrepancies for the MWR than for SP in comparison with GNSS for very high values of SZA. Finally, a marked seasonal dependence is observed for SP-GNSS differences, modulated by the IWV dependence explained above. In contrast, the seasonal dependence for MWR is quite weaker than it is for SP. Therefore, in spite of the excellent agreement found among the different instruments, it is recommended that: i) the dependence with IWV be studied and corrected to further increase the performance of the instruments, and ii) metadata be used to filter out situations in which the instrument cannot operate properly.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗

DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗

DOE OSTI2024

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗

DOE OSTI2024

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System: Preprint

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗

DOE OSTI2021

Stopping criteria for ending autonomous, single detector radiological source searches

While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a “stop search” action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2021

The Fall and Rise of the Global Climate Model

Abstract Global models are an essential tool for climate projections, but conventional coarse‐resolution atmospheric general circulation models suffer from errors both in their parameterized cloud physics and in their representation of climatically important circulation features. A notable recent study by Terai et al. (2020, https://doi.org/10.1029/2020ms002274) documents a global model capable of reproducing the regime‐based effect of aerosols on cloud liquid water path expected from observational evidence. This may represent a significant advance in cloud process fidelity in global models. Such models can be expected to give a better estimate of the effective radiative forcing of the climate. If this advance in cloud process representation can be matched by advances in the representation of circulation features such as monsoons, then such models may also be able to navigate the complex tangle between spatially heterogeneous aerosol–cloud interactions and regional circulation patterns. This tight link between aerosol and circulation results in anthropogenic perturbations of climate variables of societal importance, such as regional rainfall distributions. Upcoming global models with km‐scale resolution may improve the regional circulation and be able to take advantage of the Terai et al. (2020, https://doi.org/10.1029/2020ms002274) improvement in cloud physics. If so, an era of significantly improved regional climate projection capabilities may soon dawn. If not, then the improvement in cloud physics might spur intensified efforts on problems in model dynamics. Either way, based on the rapid changes in aerosol emissions in the near future, learning to make reliable projections based on biased models is a skill that will not go out of style.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 m resolution) and low-resolution (mostly 1000 m resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.

Tan, Zeli [Pacific Northwest National Laboratory (↗

DOE OSTI2025

Dual‐Transformer Deep Learning Framework for Seasonal Forecasting of Great Lakes Water Levels

Abstract The Great Lakes of North America form one of the largest freshwater systems on Earth, and their lake‐wide average water levels (lake levels) can fluctuate by more than 0.5 m on a seasonal scale. These fluctuations pose substantial challenges for coastal resilience, flood risk management, and navigation planning. Accurate seasonal forecasting of lake levels using traditional mechanistic models is challenging due to the complex physical mechanisms and coupled hydroclimatic processes involved. Recently, deep learning has gained prominence in geoscience applications for its ability to recognize intricate patterns within multiphysical data sets. Here, we introduce a novel Dual‐Transformer deep learning framework, tested on the Great Lakes. This architecture integrates two modified Transformer models: the Prophet, which predicts underlying trends, and the Critic, which refines the Prophet's predictions. The final lake level prediction is derived by weighting the outputs of both models through a multi‐layer perceptron, jointly trained with the Prophet and Critic to enhance overall accuracy. Our results demonstrate that the innovative learning framework achieves the highest prediction accuracy compared to established deep learning models when using identical input features. It attains a root mean square error of 4–7 cm in predicting lake levels up to 6 months in advance across the lakes. Additionally, the Dual‐Transformer model runs six orders of magnitude faster than conventional mechanistic models, producing results in less than one second on a typical personal computer. These findings suggest that our deep learning framework has strong potential to advance lake level prediction and carries important implications for water management and disaster mitigation, thereby enhancing the quality of life in coastal regions.

Chen, Yi [Great Lakes Research Center Michigan Tec↗

DOE OSTI2025

Real-Time High-Accuracy Digital Wireless Time, Frequency, and Phase Calibration for Coherent Distributed Antenna Arrays

his work presents a fully-digital high-accuracy real-time calibration procedure for frequency and time alignment of open-loop wirelessly coordinated coherent distributed antenna array (CDA) modems, enabling radio frequency (RF) phase coherence of spatially separated commercial off-the-shelf (COTS) software-defined radios (SDRs) without cables or external references such as the global navigation satellite system (GNSS). Building on previous work using high-accuracy spectrally-sparse time of arrival (ToA) waveforms and a multistep ToA refinement process, a high-accuracy two-way time transfer (TWTT)-based time–frequency coordination approach is demonstrated. Due to the two-way nature of the high-accuracy TWTT approach, the time and frequency estimates are Doppler and multipath tolerant, so long as the channel is reciprocal over the synchronization epoch. This technique is experimentally verified using COTS SDRs in a lab environment in static and dynamic scenarios and with significant multipath scatterers. Time, frequency, and phase stability were evaluated by beamforming over coaxial cables to an oscilloscope which achieved time and phase precisions of ~60– 70 ps , with median coherent gains above 99% using optimized coordination parameters, and a beamforming frequency root-mean-square error (RMSE) of 3.73 ppb in a dynamic scenario. Finally, experiments were conducted to compare the performance of this technique with previous works using an analog continuous-wave two-tone (CWTT) frequency reference technique in both static and dynamic settings.

Clock synchronization↗

DOE OSTI2024

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records

Objective: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidirectional Encoder Representations from Transformers (BERT), is motivated by the gaps in the existing deep-learning related methods, including high false negatives, sub-optimal accuracy, higher computational cost, and the risk of information loss. EHR-BERT is an innovative framework rooted in the BERT architecture, meticulously tailored to navigate the hurdles in the contemporary BERT method; thus, enhancing anomaly detection in EHRs for healthcare applications.Methods: The EHR-BERT framework was designed using the Sequential Masked Token Prediction (SMTP) method. This approach treats EHRs as natural language sentences and iteratively masks input tokens during both training and prediction stages. This method facilitates the learning of EHR sequence patterns in both directions for each event and identifies anomalies based on deviations from the normal execution models trained on EHR sequences.Results: Extensive experiments on large EHR datasets across various medical domains demonstrate that EHR-BERT markedly improves upon existing models. It significantly reduces the number of false positives and enhances the detection rate, thus bolstering the reliability of anomaly detection in electronic health records. This improvement is attributed to the model’s ability to minimize information loss and maximize data utilization effectively.Conclusion: EHR-BERT showcases immense potential in decreasing medical errors related to anomalous clinical events, positioning itself as an indispensable asset for enhancing patient safety and the overall standard of healthcare services. The framework effectively overcomes the drawbacks of earlier models, making it a promising solution for healthcare professionals to ensure the reliability and quality of health data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Radiation Mapping for an Unmanned Aerial Vehicle: Development and Simulated Testing of Algorithms for Source Mapping and Navigation Path Generation

Image reconstruction algorithms were developed for radiation source mapping and used for generating the search path of a moving radiation detector, such as one onboard an unmanned aerial vehicle. Simulations consisted of first assuming radioactive sources of varying complexity and estimating the radiation fields that would then be produced by that source distribution. Next, the "measurements" that would result from a pair of adjacent spatial locations were computed. A crude estimate of the source distribution likely to have produced such "measurements" was reconstructed based upon the limited measurements. Location of the next "measurement" was then determined as halfway between the location of the estimated source and the current "measurement." With each additional sample, improved source distribution reconstructions were made and used to inform the immediate direction of detector motion. Source reconstruction or mapping was formulated as an inverse problem solved with either maximum a posteriori or least squares (LS) regression deconvolution methods. Different amounts of noise were added to the simulated "measurements," allowing evaluation of the methods' performances as functions of signal-to-noise ratio of the measured map. As expected, methods that promote sparsity were better suited in reconstructing point sources. Reliable prior information of the source distribution also improved the reconstruction results, especially with distributed sources. With a non-negative least square algorithm and the suggested paths it generated, location of sources was successfully estimated to an accuracy of 0.014 m within nine iterations in a single-source scenario and 12 iterations in a two-source scenario, given a 10% error on the integrated counts and a Poisson distribution of the noise associated with the measured counts.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2020

Radioactive Source Localization via Bayesian Particle Filter

In the event of a misplaced radioactive source or other emergency situation, measuring a radiation field, mapping its distribution, and determining a source location are essential tasks to ameliorating the situation. However, radiation fields may be extremely hazardous to human surveyors and minimizing received radiation doses is just as essential. Robots appear to be a potential solution to these problems. Beyond simply measuring radiation, the robot's computer processing capabilities offer a way to apply complex data analysis methods to radiation measurements in real-time. Methods which predict likely source locations can then feed this information into other processes, potentially improving path planning and enabling more efficient measurements. Given a robot mounted with a gamma-ray detector, can we: develop a methodology to account for detector performance across a wide range of source angles, distances, and photon energies? operate an autonomously navigating robot to effectively survey and characterize an area of interest? implement a data analysis method, conventionally used in measurements of motion, for source localization purposes? An open-source TurtleBot 3 robot, running Robot Operating System (ROS) on Ubuntu 16.04 LTS, was fitted with a Kromek GR1{sup R} Cadmium Zinc Telluride (CZT) solid-state gamma-ray detector. As a part of ROS, the packages OpenSlam, gmapping, and amcl were used to perform Simultaneous Localization and Mapping (SLAM), determining the robot's position and mapping the surrounding area. Data was acquired via Lidar mounted on top the TurtleBot 3. Detector Calibration Fit: The equation was fit to 365 counts of various energies, distances, and angles. A MATLAB{sup R} program was written to simulate measurements taken a robot on a random walk, with count data and positions discretized into finite element pixels. Using this program, a sample of 100 runs was performed on a map with a simulated source at the center, with a total of 200 of 2 pixels each. Similarly, multiple runs of the filter were performed on recorded robot measurement data. In both simulation and real tests, when corrected for errors (particles placed outside of bounds or on the robot, and simulation-specific errors), corresponding t-tests of predicted x and y-coordinates were within a 95% confidence interval of the actual position. For the real trial, these positions are slightly skewed right in the x-axis as the robot remained mainly to the left side of the source within the sample area. These simulations demonstrate potential validity for the usage of a particle filter as method of radioactive source localization. In the future, true real-time implementation and data fusion may further augment the performance of the robot to localize lost sources. Additionally, identification of multiple sources, determination of source types, and usage of a collimator are areas to potentially be explored.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗