Publications

Recently, we have introduced a new generation of effective core potentials (ECPs) designed for accurate correlated calculations but equally useful for a broad variety of approaches. The guiding principle has been the isospectrality of all-electron and ECP Hamiltonians for a subset of valence many-body states using correlated, nearly-exact calculations. Here we present such ECPs for the 3d transition series Sc to Zn with Ne-core, i.e., with semi-core 3s and 3p electrons in the valence space. Besides genuine many-body accuracy, the operators are simple, being represented by a few gaussians per symmetry channel with resulting potentials that are bounded everywhere. The transferability is checked on selected molecular systems over a range of geometries. The ECPs show a high overall accuracy with valence spectral discrepancies typically ≈0.01-0.02 eV or better. They also reproduce binding curves of hydride and oxide molecules typically within 0.02-0.03 eV deviations over the full non-dissociation range of interatomic distances.

The U.S. Strategic Petroleum Reserve (SPR) stores crude oil in underground storage caverns that have been solution mined from salt domes. Salt falls from the sides or top of a cavern pose a potential threat to cavern and well integrity and to operational readiness. Underground storage caverns require a suspended casing, or hanging string, to extend into the bottom part of the cavern for brine injection in order to remove oil from the top of the cavern; salt falls can break hanging strings, leaving the cavern inaccessible until a well workover is performed to replace or extend the string. Detecting salt falls is difficult, as string breaks may not occur and surface pressure signals are similar to operationally induced signals. SONAR based detection is possible, but SONAR surveys are expensive and conducted infrequently. Historical records from the SPR were examined to look for possible correlations to geographic or operational causes. A library of salt fall and operational signals was developed and three case studies are presented.

Holes in germanium-rich heterostructures provide a compelling alternative for achieving spin based qubits compared to traditional approaches such as electrons in silicon. In this project, we addressed the question of whether holes in Ge/SiGe quantum wells can be confined into laterally defined quantum dots and made into qubits. Through this effort, we successfully fabricated and operated single-metal-layer quantum dot devices in Ge/SiGe in multiple devices. For single quantum dots, we measured the capacitances of the quantum dot to the surface electrodes and find that they reasonably compare to expected values based on the electrode dimensions, suggested that we have formed a lithographic quantum dot. We also compare the results to detailed self-consistent calculations of the expected potential. Finally, we demonstrate, for the first time, a double quantum dot in the Ge/SiGe material system.

In late July 2018, the Energy Storage (ES) Safety Collaborative sent a survey to their stakeholders. The survey was designed to gather input and data to "support the timely deployment of safe energy storage technologies." The survey would also help to inform decisions related to enhancing ES efforts while "streamlining opportunities for collaboration amongst all relevant stakeholders." A total of 17 questions were included in the survey: 13 multiple choice questions and 4 open response questions. A total of 51 responses were collected and presented here are some of the high-level takeaways.

A new approach for solving the electron transport equation in the upper atmosphere is derived. The problem is a very stiff boundary value problem, and to obtain an accurate numerical solution, matrix factorizations are used to decouple the fast and slow modes. A stable finite difference method is applied to each mode. This solver is applied to a simplified problem for which an exact solution exists using various versions of the boundary conditions that might arise in a natural auroral display. The numerical and exact solutions are found to agree with each other, verifying the method.

This study explores a Bayesian calibration framework for the RAMPAGE alloy potential model for Cu-Ni and Cu-Zr systems, respectively. In RAMPAGE potentials, it is proposed that once calibrated potentials for individual elements are available, the inter-species interactions can be described by fitting a Morse potential for pair interactions with three parameters, while densities for the embedding function can be scaled by two parameters from the elemental densities. Global sensitivity analysis tools were employed to understand the impact each parameter has on the MD simulation results. A transitional Markov Chain Monte Carlo algorithm was used to generate samples from the multimodal posterior distribution consistent with the discrepancy between MD simulation results and DFT data. For the Cu-Ni system the posterior predictive tests indicate that the fitted interatomic potential model agrees well with the DFT data, justifying the basic RAMPAGE assumptions. For the Cu-Zr system, where the phase diagram suggests more complicated atomic interactions than in the case of Cu-Ni, the RAMPAGE potential captured only a subset of the DFT data. The resulting posterior distribution for the 5 model parameters exhibited several modes, with each mode corresponding to specific simulation data and a suboptimal agreement with the DFT results.

In this report, we present preliminary research into nonparametric clustering methods for multi-source imagery data and quantifying the performance of these models. In many domain areas, data sets do not necessarily follow well-defined and well-known probability distributions, such as the normal, gamma, and exponential. This is especially true when combining data from multiple sources describing a common set of objects (which we call multimodal analysis), where the data in each source can follow different distributions and need to be analyzed in conjunction with one another. This necessitates nonparametric density estimation methods, which allow the data to better dictate the distribution of the data. One prominent example of multimodal analysis is multimodal image analysis, when we analyze multiple images taken using different radar systems of the same scene of interest. We develop uncertainty analysis methods, which are inherent in the use of probabilistic models but often not taken advance of, to assess the performance of probabilistic clustering methods used for analyzing multimodal images. This added information helps assess model performance and how much trust decision-makers should have in the obtained analysis results. The developed methods illustrate some ways in which uncertainty can inform decisions that arise when designing and using machine learning models.

Scope and Objectives: Kokkos Support provides cyber resources and conducts training events for current and prospective Kokkos users; In person training events are organized in various venues providing both generic Kokkos tutorials with lectures and exercises, as well as hands-on work on users applications.

Sparse matrix-matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix-matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, KKSPGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

A framework is presented for multi-material compliance minimization in the context of continuum based topology optimization. We adopt the common approach of finding an optimal shape by solving a series of explicit convex (linear) approximations to the volume constrained compliance minimization problem. The dual objective associated with the linearized subproblems is a separable function of the Lagrange multipliers and thus, the update of each design variable is dependent only on the Lagrange multiplier of its associated volume constraint. By tailoring the ZPR design variable update scheme to the continuum setting, each volume constraint is updated independently. This formulation leads to a setting in which sufficiently general volume/mass constraints can be specified, i.e., each volume/mass constraint can control either all or a subset of the candidate materials and can control either the entire domain (global constraints) or a sub-region of the domain (local constraints). Material interpolation schemes are investigated and coupled with the presented approach. The key ideas presented herein are demonstrated through representative examples in 2D and 3D.

The increasing interest in gasification and oxy-fuel combustion of biomass has heightened the need for a detailed understanding of char gasification in industrially relevant environments (i.e., high temperature and high-heating rate). Despite innumerable studies previously conducted on gasification of biomass, very few have focused on such conditions. Consequently, in this study the high-temperature gasification behaviors of biomass-derived chars were investigated using non-intrusive techniques. Two biomass chars produced at a heating rate of approximately 104 K/s were subjected to two gasification environments and one oxidation environment in an entrained flow reactor equipped with an optical particle-sizing pyrometer. A coal char produced from a common U.S. low sulfur subbituminous coal was also studied for comparison. Both char and surrounding gas temperatures were precisely measured along the centerline of the furnace. Despite differences in the physical and chemical properties of the biomass chars, they exhibited rather similar reaction temperatures under all investigated conditions. On the other hand, a slightly lower particle temperature was observed in the case of coal char gasification, suggesting a higher gasification reactivity for the coal char. A comprehensive numerical model was applied to aid the understanding of the conversion of the investigated chars under gasification atmospheres. In addition, a sensitivity analysis was performed on the influence of four parameters (gas temperature, char diameter, char density, and steam concentration) on the carbon conversion rate. The results demonstrate that the gas temperature is the most important single variable influencing the gasification rate.

Most of the commonly used seismic discrimination approaches are designed for teleseismic and regional data. To monitor for the smallest events, some of these discriminants have been adapted for local distances (<200 km), with mixed level of success. We take advantage of the variety of seismic sources, including nontraditionally studied anthropogenic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mininginduced events (MIEs), and tectonic earthquakes. We achieved a success rate of about 59%-83%. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category pairwise classification, seven of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and MIEs. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4%-14% in misclassification rates compared with Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases, and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74% compared with the rate of about 86% for two-category pairwise classification.

In a uniform superconductor, electrons form Cooper pairs that pick up the same quantum mechanical phase for their bosonic wavefunctions. This spontaneously breaks the gauge symmetry of electromagnetism. In 1962 Josephson predicted, and it was subsequently observed, that Cooper pairs can quantum mechanically tunnel between two weakly coupled superconductors that have a phase difference Φ. The resulting supercurrent is a 2π periodic function of the phase difference Φ across the junction. This is the celebrated Josephson effect. More recently, a fractional Josephson effect related to the presence of Majorana bound states — Majoranas — has been predicted for topological superconductors. This fractional Josephson effect has a characteristic 4π periodic current–phase relation. Now, writing in Nature Materials, Chuan Li and colleagues report experiments that utilize nanoscale phase-sensitive junction technology to induce superconductivity in a fine-tuned Dirac semimetal Bi_0.97Sb_0.03 and discover a significant contribution of 4π periodic supercurrent in Nb–Bi_0.97Sb_0.03–Nb Josephson junctions under radiofrequency irradiation.

We demonstrate ultra-low power cryogenic high electron mobility transistor (HEMT) amplifiers for measurement of quantum devices. The low power consumption (few uWs) allows the amplifier to be located near the device, at the coldest cryostat stage (typically less than 100 mK). Such placement minimizes parasitic capacitance and reduces the impact of environmental noise (e.g. triboelectric noise in cabling), allowing for improvements in measurement gain, bandwidth and noise. We use custom high electron mobility transistors (HEMTs) in GaAs/A1GaAs heterostructures. These HEMTs are known to have excellent performance specifically at mK temperatures, with electron mobilities that can exceed 10⁶ cm² /Vs, allowing for large gain with low power consumption. Low temperature measurements of custom HEMT amplifiers at T = 4 K show a current sensitivity of 50 pA at 1 MHz bandwidth for 5 mW power dissipation, which is an improvement upon performance of amplifiers using off-the-shelf HEMTs.

X-ray diffraction measurements to characterize phase transitions of dynamically compressed high-Z matter at Mbar pressures require both sufficient photon energy and fluence to create data with high fidelity in a single shot. Large-scale laser systems can be used to generate x-ray sources above 10 keV utilizing line radiation of mid-Z elements. However, the laser-to-x-ray energy conversion efficiency at these energies is low, and thermal x-rays or hot electrons result in unwanted background. We employ polycapillary x-ray lenses in powder x-ray diffraction measurements using solid target x-ray emission from either the Z-Beamlet long-pulse or the Z-Petawatt (ZPW) short-pulse laser systems at Sandia National Laboratories. Polycapillary lenses allow for a 100-fold fluence increase compared to a conventional pinhole aperture while simultaneously reducing the background significantly. This enables diffraction measurements up to 16 keV at the few-photon signal level as well as diffraction experiments with ZPW at full intensity.

We propose a functional integral framework for the derivation of hierarchies of Landau-Lifshitz-Bloch (LLB) equations that describe the flow toward equilibrium of the first and second moments of the magnetization. The short-scale description is defined by the stochastic Landau-Lifshitz-Gilbert equation, under both Markovian or non-Markovian noise, and takes into account interaction terms that are of practical relevance. Depending on the interactions, different hierarchies on the moments are obtained in the corresponding LLB equations. Two closure Ansätze are discussed and tested by numerical methods that are adapted to the symmetries of the problem. Our formalism provides a rigorous bridge between the atomistic spin dynamics simulations at short scales and micromagnetic descriptions at larger scales.

Our goal was to develop an integrated platform for electrical control of SiV defects in diamond. The understanding and techniques we discover for electrical control have direct relevance for scalable color center based devices. More fundamentally, they can serve as a basis for developing diamond light sources and exploring color center transitions previously understood as inaccessible. While we did not meet all these goals we did develop a unique set of capabilities that allowed Sandia to distinct itself both internally and through continuing external collaborations.

Physically unclonable functions are physical entities or devices that generate unique, unpredictable responses to inputs. They are important in many security applications, including encryption, authentication, anti-counterfeiting, etc. Physical unclonable functions are based on the unavoidable randomness in the manufacturing processes and are impossible to duplicate, even by the original manufacturer. In this project, we studied the feasibility of using hardened SmCo nanomagnets as the physical implementation of physically unclonable functions. Hardened SmCo nano-magnets were fabricated through a lift-off process as well as an etch-back process. The magnetization of these nano-magnets was mapped out as a function of shapes, dimensions, and processing conditions, using magnetic force microscopy. A systematic, uncontrolled bias in the polarity was identified. Attempts to mitigate this bias were made but were unsuccessful. Nevertheless, we found in the process that blanket SmCo films themselves may serve as the desired physically unclonable functions.

Fundamental investigations of non-equilibrium gas-liquid interfaces at elevated pressure will require knowledge of gas-phase boundary conditions affecting the interface structure. To assess the feasibility of applying one-dimensional imaging of spontaneous Raman scattering to resolve species and temperature gradients in the gas-phase boundary layer above a fluoroketone liquid surface, spectra of fluoroketone (1,1,1,2,2,4,5,5,5-nonafluoro-4-(trifluoromethyl)-3-pentanone) vapor in a carrier gas (N₂ or CO₂) are reported over the temperature range 300 K to 700 K. The measured Raman spectra show no detectable broadband interference from laser-induced fluorescence. Features of the fluoroketone Raman spectrum overlap the CO₂ spectrum, such that crosstalk corrections will be necessary for quantitative concentration measurements of CO₂ in mixtures. High-resolution Raman spectra in this overlap region, acquired over the same temperature range, will enable future development of temperature dependent spectral libraries for fluoroketone.

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Sandia National Laboratories (SNL) is investigating photovoltaic (PV) cell configurations, integrating them with the battery-operated Remotely Monitored Sealing Array (RMSA), and testing and evaluating performance for enhanced battery life under various environmental conditions at the K-Area Material Storage (KAMS) facility at the Savannah River Site (SRS). Unattended safeguards equipment (e.g. seals) incorporates many low-power electronic circuits, which are often powered by expensive and environmentally toxic lithium batteries. These batteries must periodically be replaced, adding a radiological hazard for both safeguards inspectors and operators. An extended field test of these prototype PV energy harvesting (EH) RMSAs at an operational nuclear facility will give additional data and allow for an analysis of this technology in a variety of realistic conditions, which will be documented in a final report. RMSAs are used for this testing, but SNL envisions energy harvesting technology may be applicable to other safeguards equipment.

This report summarizes the work performed under the Sandia LDRD project "Eyes on the Ground: Visual Verification for On-Site Inspection." The goal of the project was to develop methods and tools to assist an IAEA inspector in assessing visual and other information encountered during an inspection. Effective IAEA inspections are key to verifying states' compliance with nuclear non-proliferation treaties. In the course of this work we developed a taxonomy of candidate inspector assistance tasks, selected key tasks to focus on, identified hardware and software solution approaches, and made progress in implementing them. In particular, we demonstrated the use of multiple types of 3-d scanning technology applied to simulated inspection environments, and implemented a preliminary prototype of a novel inspector assistance tool. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication. Approved for public release; further dissemination unlimited.

Trusting simulation output is crucial for Sandia's mission objectives. We rely on these simulations to perform our high-consequence mission tasks given our treaty obligations. Other science and modelling needs, while they may not be high-consequence, still require the strongest levels of trust to enable using the result as the foundation for both practical applications and future research. To this end, the computing community has developed work- flow and provenance systems to aid in both automating simulation and modelling execution, but to also aid in determining exactly how was some output created so that conclusions can be drawn from the data. Current approaches for workflows and provenance systems are all at the user level and have little to no system level support making them fragile, difficult to use, and incomplete solutions. The introduction of container technology is a first step towards encapsulating and tracking artifacts used in creating data and resulting insights, but their current implementation is focused solely on making it easy to deploy an application in an isolated "sandbox" and maintaining a strictly read-only mode to avoid any potential changes to the application. All storage activities are still using the system-level shared storage. This project was an initial exploration into extending the container concept to also include storage and to use writable containers, auto generated by the system, as a way to link the contained data back to the simulation and input deck used to create it.

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This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to identify precursors providing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub-missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.

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Decoupling the electronic thermal and electrical conductivities is one of the limitations hindering a breakthrough in thermoelectric efficiency. After a conformal surface coating of bismuth telluride nanowires (Bi2Te3 NWs) by porphyrins, the thermal conductivity increases from 0.8 to 1.0 Wm-1K-1 at 300 K without any obvious change in electrical conductivity. Density Functional Theory (DFT) calculations assisted by Boltzmann Transport Equation (BTE) simulations of electronic transport properties indicate that the electronic thermal transport is enhanced by the depletion of surface charge carriers, which results in transition from metallic to semiconducting behavior. Thus, the adsorption of porphyrin onto the Bi2Te3 NWs layer suppresses the surface electronic conduction, resulting in thermal electronic conduction dictated by the bulk of the NW. The results mean that electronic thermal transport can be decoupled from the electrical conductivity by changing the density of surface states on Bi2Te3 NWs.

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Reproducibility is an essential ingredient of the scientific enterprise. The ability to reproduce results builds trust that we can rely on the results as foundations for future scientific exploration. Presently, the fields of computational and computing sciences provide two opposing definitions of reproducible and replicable. In computational sciences, reproducible research means authors provide all necessary data and computer codes to run analyses again, so others can re-obtain the results (J. Claerbout et al., 1992). The concept was adopted and extended by several communities, where it was distinguished from replication: collecting new data to address the same question, and arriving at consistent findings (Peng et al. 2006). The Association of Computing Machinery (ACM), representing computer science and industry professionals, recently established a reproducibility initiative, adopting essentially opposite definitions. The purpose of this report is to raise awareness of the opposite definitions and propose a path to a compatible taxonomy.

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This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processes for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.

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This document, the Sandia National Laboratories lntegrated Facilities and lnfrastructure Plan, summarizes long-, mid-, and short-term site planning to support Sandia's mission. High-level direction that influences site development includes the Stockpile Stewardship and Management Plan (SSMP) and Nuclear Posture Review (NPR), while the relationship to National Nuclear Security Administration (NNSA) infrastructure is described in the NNSA Master Asset Plan (MAP). Using the SSMP as an indication of the future workload informs the planning process for facilities and infrastructure. Figure 1 shows the outlook of the NNSAs nuclear weapons modernization programs through the year 2042, which indicates a stable, slightly growing workload.

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Funded through the IHNS/E&HS investment area for FY16-18, the RAPIER LDRD sought to evaluate the potential benefits and applicability of the new Oxford MinION nanopore sequencer to pathogen diagnostic applications in biodefense, biosurveillance, and global/public health. The project had four primary objectives: 1) to investigate the performance of the MinION sequencer while building facility with its operation, 2) to develop microfluidic library prep automation facilitating the use of the MinION in field-forward or point-of-care applications, 3) to leverage CRISPR/Cas9 technology to enable targeted identification of bacterial pathogens, and 4) to capitalize on the real- time data output capabilities of the MinION to enable rapid sequence-based diagnostics. While the rapid evolution of the MinION sequencing technology during the course of the project posed a number of challenges and required a reassessment of initial project priorities, it also provided unique opportunities, notably culminating in our development of the RUBRIC real-time selective sequencing software.

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ParaView Catalyst is an API for accessing the scalable visualization infrastructure of ParaView in an in-situ context. In-situ visualization allows simulation codes to access data post-processing operations while the simulation is running. In-situ techniques can reduce data post-processing time, allow computational steering, and increase the resolution and frequency of data output. For a simulation code to use ParaView Catalyst, adapter code needs to be created that interfaces the simulations data structures to ParaView/VTK data structures. Under ATDM, Catalyst is to be integrated with SPARC, a code used for simulation of unsteady reentry vehicle flow.

Running visualization and analysis algorithms on ATS-1 platforms is a critical step for supporting ATDM apps at the exascale level. We are leveraging VTK-m to port our algorithms to the ATS-specific hardware and ensuring that it runs well.

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This project explored a new capability for studying collisions of electrons and molecules with unprecedented accuracy by combining high electron-energy resolution with velocity mapped imaging of electrons. Low-energy electrons were produced within a supersonic beam by photoionization of metastable krypton using a dye laser to generate electrons with tunable kinetic energy and a narrow energy spread. A new configuration for electron imaging optics was developed to enable scattering of electrons in a zero-field environment with subsequent rapidly pulsed velocity mapped imaging of the electrons. Development of this new capability will significantly enhance DOE/NNSA's ability to perform basic research on processes relevant to plasmas in atmospheric re-entry and neutron generation for weapons systems and provide fundamental understanding of electron-driven chemistry important to solar energy conversion.

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Sandia National Laboratories has tested and evaluated two Nanometrics Centaur digitizers. The Centaur digitizers are intended to record sensor output for seismic and infrasound monitoring applications. The purpose of this digitizer evaluation is to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self- noise, dynamic range, system noise, response, passband, and timing. The Centaur digitizers are being evaluated for potential use in the International Monitoring System (IMS) of the Comprehensive Nuclear Test-Ban-Treaty Organization (CTBTO).

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The research and development of new algorithmic and statistical methods of outbreak detection is an ongoing research priority in the field of biosurveillance. The early detection of emergent disease outbreaks is crucial for effective treatment and mitigation. New detection methods must be compared to established approaches for proper evaluation. This comparison requires biosurveillance test data that accurately reflects the complexity of the real-world data it will be applied to. While the test and evaluation of new detection methods is best performed on real data, it is often impractical to obtain such data as it is either proprietary or limited in scope. Thus, scientists must turn to synthetic data generation to provide enough data to properly eval- uate new detection methodologies. This paper evaluates three such synthetic data sources: The WSARE dataset, the Noufilay equation-based approach, and the Project Mimic data generator.

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