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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2023

Deployment of ADTimePix3 areaDetector Driver at Neutron and X-ray User Facilities

TimePix3 is a 65k hybrid pixel readout chip with simultaneous Time-of-Arrival (ToA) and Time-over-Threshold (ToT) recording in each pixel*. The chip operates without a trigger signal with a sparse readout where only pixels containing events are read out. The flexible architecture allows 40 MHits/s/cm² readout throughput, using simultaneous readout and acquisition by sharing readout logic with transport logic of superpixel matrix formed using 2x4 structure. The chip ToA records 1.5625 ns time resolution. The X-ray and charged particle events are counted directly. However, indirect neutron counts use 6Li fission in a scintillator matrix, such as ZnS(Ag). The fission space-charge region is limited to 5-9 um. A photon from scintillator material excites a photocathode electron, which is further multiplied in dual-stack MCP. The neutron count event is a cluster of electron events at the chip. We report on the EPICS areaDetector** ADTimePix3 driver that controls Serval*** using json commands. The driver directs data to storage and to a real-time processing pipeline and configures the chip. The time-stamped data are stored in raw .tpx3 file format and passed through a socket where the clustering software identifies individual neutron events. The conventional 2D images are available as images for each exposure frame, and a preview is useful for sample alignment. The areaDetector driver allows integration of time-enhanced capabilities of this detector into SNS beamlines controls and unprecedented time resolution.

Gofron, Kaz↗
DOE OSTI2020

In situ monitoring of the stopped muon flux at Mu2e

The specific object of Mu2e search is the neutrino-less coherent $\mu^-\rightarrow e^-$ conversion in the field of an aluminum nucleus and the signal is a monoenergetic electron of energy $ \approx 104.97$ MeV. This process is forbidden in the Standard Model but allowed in many of its extensions: with minimal changes to include neutrino masses and oscillation, the Branching Ratio of this process, or the similarly interesting $\mu\rightarrow e \gamma$ decay, is expected to be of the order of $\mathcal{O}(10^{-54})$. Values like these are below any currently achievable experimental sensitivity, and the observation of a signal would be unambiguous evidence of New Physics. The upper limit on the muon conversion was set by SINDRUM II at $7\times10^{-13}$ (90\% C.L.) and the goal of the Mu2e collaboration is an improvement of 4 order of magnitudes.\\ The Mu2e experiment can be conceptually divided into three stages: interaction of the primary proton pulse with the tungsten target a nd production of $\pi$ and $\mu$; collection and transport of the produced particles down to the aluminum stopping target; interaction with the stopping target and measurement of the output particles. The measurement of the stopped muon flux is of cardinal importance for the normalization of the Mu2e results. The baseline design includes two detectors developed to measure this flux, counting $\gamma$ emitted by the muonic atoms: HPGe and LaBr$_3$(Ce). The studies performed by the Mu2e Collaboration show that these two systems can be reliably used to determine the overall normalization.\\ The number of stopped muons is proportional to the number of protons on target, which itself depends on the extraction system for the proton beam. The Mu2e beam delivery system will use a resonant extraction to create the proton pulses and this method is characterized by intensity fluctuations on the time scale of milliseconds. These fluctuations, aside from the proportional fluctuation of t he number of muons, have a non-trivial impact on the overall! performance of the apparatus. For example, higher intensity proton bunches translate into higher veto rates by the Cosmic Rays Veto system, which is triggered by secondary particles generated by the proton interaction. Another example is the effect of the instantaneous luminosity on the reconstruction efficiency: higher intensity translates into higher detector occupancy, which in turn reduces the reconstruction efficiency.\\ The two cited detectors encounter limitations when trying to monitor the flux at the millisecond timescales: HPGe is by construction a slow detector while the rate of emission of the $\gamma (1809\textrm{ keV})$ the LaBr$_3$(Ce) will measure is too low. As of today, no system allows to monitor these fluctuations and the goal of this Thesis is to try filling this gap. The method we developed to monitor the fluctuations of the stopped muon flux, and by extension, the fluctuation in the proton pulses intensity is based on counting the number of muons capt ured in the stopping target by counting the number of protons produced in the process of the nuclear muon capture. This is one of the possible processes a stopped muon can undergo and it can lead to the ejection of charged particles.\\ The reconstruction algorithms were developed by the Mu2e collaboration with the explicit (and sometimes implicit) aim to reconstruct tracks of electrons in an energy range of a conversion electron. The protons we are interested in behave quite differently from these electrons and the cardinal task of this study has been to tailor the reconstruction routines to these particles. This has been done by analyzing simulated events consisting of a single proton to optimize the procedure and understand the features of these tracks. A major difference with respect to the electrons is the non-relativistic nature of $100\div300$ MeV$/c$ protons: accounting for the low velocity is both a challenge and a unique signature of the tracks.\\ The use of a pulse d beam and the need to allow produced $\pi$ to decay and $\m! u$ to int! eract in the stopping target forces the definition of a Mu2e \textit{event}: an event is comprised by everything which happens in a $1.7\ \mu$s window between the incidence two consecutive proton pulses. This means that the reconstruction will be performed in an environment crowded by many different particles. The next step of this study has been to estimate the performance of the method in a fully-fledged Mu2e simulated event and to study what kind of selections would improve it.\\ Our results are satisfactory since the number of reconstructed protons is significant (few per event), and show that a monitor on the timescale of milliseconds is indeed possible.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

SIESTA a Fatigue Solution: User's and Theory Manual

This report documents the fatigue code SIESTA that has been used for recently here at Sandia National Laboratories. It is written in two parts: the first as a user manual and the second as a theory manual. Currently employed in SIESTA are stress-life cycle approaches. Clients have requested the use of standards in particular analyses; therefore, the American Society of Mechanical Engineers, Boiler Pressure Vessel Code fatigue standards have been implemented. These include an elastic, an elastic-plastic, and a weld fatigue method. All three methods use a Max-Min cycle counting method that is appropriate for non-proportional loading. A Signed von Mises method that used a Rainflow Cycle Counting Method is also implemented. The Signed von Mises with the Rainflow Cycle Counting Method is appropriate for proportional loading. Several verification examples are noted and include comparisons to experimental data.

42 ENGINEERING↗
DOE OSTI2021

High Data Rate Detector for Neutron Reflectometer

Neutron reflectometers are a class of instruments that employ neutron diffraction for measuring the structure of thin films. The technique provides valuable information over a wide variety of scientific and technological applications including chemical aggregation, polymer and surfactant adsorption, structure of thin film magnetic systems, biological membranes, etc. Such instruments are considered to be one of the most important in the neutron science field. Newer facilities place growing demands on the count rate capability of the specialized neutron detectors required. Proportional Technologies completed the Phase I effort successfully with a fully operational prototype detector built at PTI and tested at the CG1A neutron beam of the High Flux Isotope Reactor (HFIR, Oak Ridge, TN). The prototype incorporated 3 panels of 14 copper-walled cells each (total of 42 cells), each lined with 1.3 μm of 10B4C. The detector was tested for efficiency, spatial resolution, count rate linearity, uniformity, overlap and scatter. Testing revealed stable operation, uniform response across cells, and successful resolution of individual cells. The maximum count rate tested in the 3 panels combined was 590 kHz, limited by a deadtime of about 1 μs in the digital electronics. The local rate measured in a single cell showed no deadtime losses up to 810 cps/mm2, indicating that a rate of 20 Mhz can be supported in the 308-cell detector planned for Phase II.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Historic Source Inspection Records

To create true counts of number of containers, each container can be identified by a combination of the purchase order, unique part number and type of inspection. The column TestEventName and any columns derived from TestEventName should not be used in the instance of presource inspection because the vendor may mete out testing over multiple months, meaning that a container would be counted twice if a single test was completed in one month and another test in a different month. By counting on this combination of values, we are able to drop row entries that signify multiple tests completed on a single container. Inspection type must be used because each container will be tested by the vendor and re-tested by the LANL container engineering team. Recall that the vendor will complete action limit tests for the full set of containers during pre-source inspection but during source inspection, action limit tests are completed for only a random subset. However, LANL SMEs will complete a manual inspection for the full set of containers.

42 ENGINEERING↗
DOE OSTI2024

Evaluation of XCT for Matrix Density Measurement of Particle Fuel Forms

Particle fuel forms generally consist of a dispersion of fuel, such as tristructural isotropic (TRISO) particles, within a refractory matrix (e.g., graphite or silicon carbide). The density of matrix materials for particle fuel forms is of interest for modeling fuel form strength and thermal properties and may be specified as a quality control parameter, depending on reactor design. Some of the uncertainty associated with traditional, manual approaches can be eliminated by performing x-ray computed tomography (XCT) on the fuel forms and applying image processing methods to generate a precise count of the number of particles. This also removes the need to include determination of particle count within each individual fuel form during fabrication. Unfortunately, reconstruction artifacts from high-Z uranium-bearing kernels prevent accurate measurement of individual particle volumes using this approach, so the use of mean particle mass and volume are still necessary for computation of average fuel form matrix density. This method of using XCT to count particles in individual fuel form for determination of average matrix density was applied to three archived compacts from the Advanced Gas Reactor Fuel Development and Qualification (AGR)-1 campaign, four archived compacts with uranium carbide/uranium oxide (UCO) TRISO from the AGR-2 campaign, and three archived UO 2 -TRISO compacts from the AGR-2 campaign. The resulting density values were compared with those previously reported, showing slight changes due to uncertainties in the previously used number of particles in each of these cylindrical, graphite matrix compacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Field Test Report Neutron Scintillator Array Dry Storage Cask Scanner FY2024

During two weeks of Field Testing at the Idaho National Laboratory INTEC Cask Farm in July and August 2024, the LLNL Dry Storage Cask Scanner Array was lifted on top of an MC-10 dry storage fuel cask and operated to acquire neutron and gamma-ray data from the 24 fuel bundle positions. Neutron and gamma-ray data acquisition scans across the top of the cask of varying dwell times were performed July 15-18, 2024 and August 19-22, 2024 to evaluate the ability of the scanner data to reveal asymmetries in the fuel positions that reflect asymmetries in the MC-10 cask fuel bundle loading. The MC-10 cask 24 position fuel bundle loading at the INTEC Cask Farm is well documented, including the locations of six empty fuel bundle positions. This loading presents an opportunity to test the ability of the scanner system to detect diversion of spent fuel bundles as well as to validate the MC-10 cask MCNP modeling. The cask scanner array consists of six Stilbene crystal scintillator detectors and a linear actuator frame that moves the six detectors across the MC-10 dry storage cask to obtain data above each of the 24 fuel bundle positions. The detectors are connected to a pulse-shape discrimination data acquisition system capable of generating separate neutron and gamma-ray spectra for each detector and for each scan position. From the prior single detector Field Test in 2021 and iteration with MCNP modeling, the neutron and gamma-ray data were analyzed in multiple energy regions to identify an analysis method that would provide the strongest and most consistent signature of the asymmetric MC-10 cask fuel loading1 . From both the 2021 Field Test and the current Field Test results, the neutron capture gamma-ray count rate around 2.2 MeV provides the strongest signature of the asymmetric MC-10 cask fuel loading and has qualitative agreement with MCNP calculations. Counting all gamma-rays produces a similar signature. Neutrons emerging from the cask top are moderated and captured by the hydrogen in the polyethylene moderator and scintillator detector, producing a 2.2 MeV gamma ray which is seen in the scintillator gamma-ray spectrum. The count rate in the 2.2 MeV gamma-ray region is ~50 c/s, which is ~1000x higher than the ~0.05 n/s rate in the > 4MeV neutron region, and ~50x greater than the ~1 n/s rate in the neutrons > 500 keV region. Analysis of the 2.2 MeV neutron-capture Compton-scattered gamma-rays produces a statistically significant signature of the INTEC Cask Farm MC-10 asymmetric fuel loading. MCNP simulations indicate that the average neutron energy spectrum offers the potential to detect a large asymmetry from several missing bundles as well as individual missing fuel bundles. Testing this feature will require measurements on a cask with single missing elements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Integrated Spatial, Spectral, & Temporal Optical Reflectance System for Precision Occupancy & Location Sensing to Improve Building Energy Efficiency

Buildings consume approximately 35% of the electricity used in the U.S. and building owners can significantly reduce this energy use by providing services like heating, electrical power and lighting only when people are present. The ARPAe funded program titled “INTEGRATED SPATIAL, SPECTRAL, & TEMPORAL OPTICAL REFLECTANCE SYSTEM FOR PRECISION OCCUPANCY & LOCATION SENSING TO IMPROVE BUILDING ENERGY EFFICIENCY” demonstrates how a low cost sensor technology developed for measuring distances can be used to count and locate occupants with a high degree of precision with a very low error rates. This platform tells a building control system where occupants are located (but not who they are) so that energy consuming services can be provided only when the services are needed by building occupants. The original proof of concept involved using low cost, commercially available time-of-flight (TOF) sensors that measure distance, but the performance of these existing sensors was lacking, as they could not operate properly in the presence of sunlight, which blinded the simple TOF sensors and limited their utility in buildings. This project proposed a powerful new class of TOF sensors that used state-of-the-art integrated circuit (IC) fabrication processes that combined advanced photonics with conventional silicon chip circuitry for improved sensor performance. An equally important part of this project was to find ways to maximize occupant count and location accuracy while using the fewest number of sensors possible, in order to keep costs low. By using building blueprints to create digital twins of commercial building spaces, the team developed new algorithms to maximize occupant count and tracking accuracy by properly locating the minimum number of sensors at just the right spots in the building. This capability not only minimizes system costs but also simplified sensor installation and system commissioning. Our simulations of our sensor networks for a range of commercial floorplan designs demonstrated that our installed cost target of $0.08/sqft was attainable, though not fully demonstrated during the project. Finally, we noted that the TOF sensor concept could provide a valuable role in health and eldercare by tracking patients without the need for worn sensors and would be useful for fall detection and other patient safety metrics, including tracking healthcare/patient interactions. We feel that, when fully developed, this new class of sophisticated TOF sensors and support software will be a powerful new approach to improving building energy efficiency based on occupant centric control platforms and will also open new levels of patient safety in healthcare operations. To realize this potential, the team formed the Troy Sensor Company LLC to oversee licensing of the programs patents and continue to seek commercialization of this program’s activity sensing technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Gamma and Neutron Measurement and Modeling of Irradiated TRISO Fuel

Given the unique characteristics of the PBR fuel cycle, both gamma and neutron measurements are expected to play important roles in performing and maintaining nuclear material control and accounting for spent pebbles to safeguard the fuel cycle. Given the lack of irradiated pebbles in the US, a variety of irradiated TRISO fuel samples with wide ranges of burnups and cooling times available at ORNL were used in this work. A large number of gamma and neutron measurements have been performed on these samples to collect data to test the various detectors and to benchmark the computer models to simulate the depletion and decay of the fuel and the measurements themselves. Two neutron detectors, including a custom-made detector and the Very High-Performance Neutron Multiplicity Counting, were used to measure the neutrons emitted by these TRISO samples. Three gamma spectrometry detectors, including an HPGe and the M400 CZT detector, were used to measure gamma-ray emissions from these samples. The M400 was recently adopted by the IAEA for fresh uranium measurements, but it was tested for spent fuel measurements prior to this project. Detailed MCNP models were developed to simulate these neutron and gamma measurements. Some GADRAS models were also developed to cross check the MCNP models for the gamma measurements. It was found challenging to perform neutron measurements in the hot cell due to the high background counts. Close agreements were observed between the simulated and measured neutron count rates in both detectors’ measurements of californium calibration sources. Both the HPGe and M400 detectors were able to measure the 604 and 662 keV peaks from these samples, which are the two most important peaks used to infer fuel burnup. Although the M400 detector did not have nearly good energy resolution and did not detect some of the minor peaks as the HPGe detector, it was found to be capable of handling significantly higher dose rates than HPGe. Given the complexities in the TRISO samples (e.g., different samples sizes) and uncertainties in the alignments between the detector and the TRISO fuel inside the containers, large scatters were found between the peak area rates and the samples’ burnups. However, the 604/662 peak ratios were found to trend well with the samples’ burnups among most samples in both measured and simulated results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Poisson-response Tensor-on-Tensor Regression and Applications

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as inter national relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluating User Errors and Temporal Trends in Marine Fish Communities Using 360-Degree Underwater Photography

The use of environmental DNA (eDNA) sampling has been proposed as a complementary method to monitor fish species in marine environments, offering a non-invasive and potentially more efficient approach to marine species observations. eDNA monitoring could be especially useful in and around sites targeted for marine energy generation as these regions need regular monitoring that would be impractical with traditional techniques. Before we can fully rely upon eDNA, we must first verify its accuracy against other proven methods, such as the use of underwater photography. In this study, I deployed a 360-degree camera in the tidal channel of Sequim Bay once a month during several hours overlapping slack tide. I investigated how having multiple people identify and count fish on underwater images could affect the overall results. Using chi square tests in R, I compared my fish identifications and counts to those made by another intern on the same images recorded in August. I found significant differences in the number of species identified and the total individual counts between the two different datasets. I also tested the statistical differences in both Shannon diversity and Pielou evenness indices between the August, September, and November camera deployments using a Hutcheson t-test. Only one significant difference was found in the Shannon index comparisons, and none were found between the Pielou evenness comparisons. These findings show that if multiple identifiers are used to process underwater images, quality control checks must be made to reduce the potential for error. This also points toward the possibility to leverage more advanced image analysis processes, such as automated image analysis software. The findings from this study also show that the dynamics of marine fish communities can vary over a few months; however, further analysis is needed to determine the extent of the seasonal changes in Sequim Bay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Investigation of microorganisms in cannabis after heating in a commercial vaporizer

There are concerns about microorganisms present on cannabis materials used in clinical settings by individuals whose health status is already compromised and are likely more susceptible to opportunistic infections from microbial populations present on the materials. Most concerning is administration by inhalation where cannabis plant material is heated in a vaporizer, aerosolized, and inhaled to receive the bioactive ingredients. Heating to high temperatures is known to kill microorganisms including bacteria and fungi; however, microbial death is dependent upon exposure time and temperature. It is unknown whether the heating of cannabis at temperatures and times designated by a commercial vaporizer utilized in clinical settings will significantly decrease the microbial loads in cannabis plant material. To assess this question, bulk cannabis plant material supplied by National Institute on Drug Abuse (NIDA) was used to assess the impact of heating by a commercial vaporizer. Initial method development studies using a cannabis placebo spiked with Escherichia coli were performed to optimize culture and recovery parameters. Subsequent studies were carried out using the cannabis placebo, low delta-9 tetrahydrocannabinol (THC) potency and high THC potency cannabis materials exposed to either no heat or heating for 30 or 70 seconds at 190°C. Phosphate-buffered saline was added to the samples and the samples agitated to suspend the microorganism. Microbial growth after no heat or heating was evaluated by plating on growth media and determining the total aerobic microbial counts and total yeast and mold counts. Overall, while there were trends of reductions in microbial counts with heating, these reductions were not statistically significant, indicating that heating using standard vaporization parameters of 70 seconds at 190°C may not eliminate the existing microbial bioburden, including any opportunistic pathogens. When cultured organisms were identified by DNA sequence analyses, several fungal and bacterial taxa were detected in the different products that have been associated with opportunistic infections or allergic reactions including Enterobacteriaceae, Staphylococcus, Pseudomonas, and Aspergillus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Front-End Design for SiPM-Based Monolithic Neutron Double Scatter Imagers

Neutron double scatter imaging exploits the kinematics of neutron elastic scattering to enable emission imaging of neutron sources. Due to the relatively low coincidence detection efficiency of fast neutrons in organic scintillator arrays, imaging efficiency for double scatter cameras can also be low. One method to realize significant gains in neutron coincidence detection efficiency is to develop neutron double scatter detectors which employ monolithic blocks of organic scintillator, instrumented with photosensor arrays on multiple faces to enable 3D position and multi-interaction time pickoff. Silicon photomultipliers (SiPMs) have several advantageous characteristics for this approach, including high photon detection efficiency (PDE), good single photon time resolution (SPTR), high gain that translates to single photon counting capabilities, and ability to be tiled into large arrays with high packing fraction and photosensitive area fill factor. However, they also have a tradeoff in high uncorrelated and correlated noise rates (dark counts from thermionic emissions and optical photon crosstalk generated during avalanche) which may complicate event positioning algorithms. We have evaluated the noise characteristics and SPTR of Hamamatsu S13360-6075 SiPMs with low noise, fast electronic readout for integration into a monolithic neutron scatter camera prototype. The sensors and electronic readout were implemented in a small-scale prototype detector in order to estimate expected noise performance for a monolithic neutron scatter camera and perform proof-of-concept measurements for scintillation photon counting and three-dimensional event positioning.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Two-point Correlation Function Studies for the Milky Way: Discovery of Spatial Clustering from Disk Excitations and Substructure

We introduce a two-particle correlation function (2PCF) for the Milky Way, constructed to probe spatial correlations in the orthogonal directions of the stellar disk in the Galactic cylindrical coordinates of R, ϕ, and z. We use this new tool to probe the structure and dynamics of the Galaxy using the carefully selected set of solar neighborhood stars (d ≲ 3 kpc) from Gaia Data Release 2 that we previously employed for studies of axial symmetry breaking in stellar number counts. We make additional, extensive tests, comparing to reference numerical simulations, to ensure our control over possibly confounding systematic effects. Supposing either axial or north–south symmetry, we divide this data set into two nominally symmetric sectors and construct the 2PCF, in the manner of the Landy–Szalay estimator, from the Gaia data. In so doing, working well away from the midplane region in which the spiral arms appear, we have discovered distinct symmetry-breaking patterns in the 2PCF in its orthogonal directions, thus establishing the existence of correlations in stellar number counts alone at subkiloparsec length scales for the very first time. In particular, we observe extensive wavelike structures of amplitude greatly in excess of what we would estimate if the system were in a steady state. We study the variations in these patterns across the Galactic disk, and with increasing ∣z∣, and we show how our results complement other observations of non-steady-state effects near the Sun, such as vertical asymmetries in stellar number counts and the Gaia snail.

79 ASTRONOMY AND ASTROPHYSICS↗