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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

BioWatch SMS Supervisor Guide

The BioWatch Sample Management System is software developed by Los Alamos National Laboratory to support the Department of Homeland Security BioWatch program. Field Operations Supervisors use the software to record, track, and manage data for the BioWatch sample collection process. This includes installing the software, creating and updating databases, assigning user IDs and passwords, editing and customizing reports, correcting data errors, recovering lost data, and troubleshooting. The BioWatch SMS Field Operations Supervisor Guide provides procedures for each task a Field Operations Supervisor performs. The reference information in this guide includes appendices of error messages and solutions, terms used interchangeably, and safety warnings along with a glossary, an index, and a list of suggested reference documents.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Parton Dynamics Inferred from High-Mass Drell-Yan Dimuons Induced by 120 GeV p+D Interactions

Fermilab Experiment 906/SeaQuest (E906/SeaQuest) is the latest in a well established tradition of studying leptoproduction from the annihilation of a quark and anti-quark, known as the Drell-Yan process. The broad goal of E906/SeaQuest is measuring various properties of nucleon structure in order to learn more about quarks and Quantum Chromodynamics (QCD), the mathematical description of the strong force. The present work investigated violations of the Lam-Tung relation between virtual photon polarization and quark and lepton angular momentum. The violation of Lam-Tung can be explained as the signature of quark-nucleon spin-orbit coupling through the use of the Transverse-Momentum-Dependent (TMD) framework, which assumes that the initial transverse momentum of quarks is smaller than the hard scattering scale, but also non-negligible. An analysis of the angular moments in Drell-Yan collected by E906/SeaQuest was performed with four different configurations in order to estimate the systematic errors attributed to each correction. After correction for background and error propagation, the final extraction of the azimuthal moment excluding contributions from the trigger was ν = 0.151 ± 0.88(stat.) ± 0.346(syst.) at an average transverse momentum of 0.87 ± 0.50 GeV/c and an average dimuon mass of 5.48 ± 0.70 GeV. In the future, the magnitude of the systematic errors on the extraction could potentially be reduced by improving the quality of the trigger efficiency calculation, improving the intensity dependent event reconstruction efficiency, considering the changes in acceptance due to a beam shift relative to the E906/SeaQuest spectrometer, and improving the modeling of background.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Scattering Angular Distributions in the ENDF/B Nuclear Data Library

Nuclear reactors function by controlling the chain reaction of fission events in a fissile material. Understanding the gain and loss of neutrons is crucial for controlling this chain reaction. The role of fission in liberating neutrons from nuclei is well known. The fact that neutrons can scatter out of a reactor and therefore be lost is less well appreciated. Scattering can happen within fissile fuel or off of any number of the non-fissile structural components holding a reactor together. The scattering angular distributions for reactor material are the quantity that give the probability for a particle (usually a neutron) to scatter off a nucleus into a given angle. The Evaluated Nuclear Data File/B (ENDF/B) nuclear data library is the most complete and authoritative reference for neutron scattering data, containing reaction cross sections and outgoing particle distributions (including scattering angular distributions). In this project, we will compare the scattering angular distributions given in these ENDF/B data sets with those that can be derived using the Blatt-Biedenharn formalism and the resolved resonance data also in the ENDF/B files. More comparisons between the scattering angular distributions of relevant isotopes will help identify and correct any errors in the ENDF/B files. The results of this study will be an important component of the next major release of the ENDF/B library.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

SAM Photovoltaic Model Technical Reference 2016 Update

This manual describes the photovoltaic performance model in the System Advisor Model (SAM) software, Version 2016.3.14 Revision 4 (SSC Version 160). It is an update to the 2015 edition of the manual, which describes the photovoltaic model in SAM 2015.1.30 (SSC 41). This new edition includes corrections of errors in the 2015 edition and descriptions of new features introduced in SAM 2016.3.14, including: 3D shade calculator Battery storage model DC power optimizer loss inputs Snow loss model Plane-of-array irradiance input from weather file option Support for sub-hourly simulations Self-shading works with all four subarrays, and uses same algorithm for fixed arrays and one-axis tracking Linear self-shading algorithm for thin-film modules Loss percentages replace derate factors. The photovoltaic performance model is one of the modules in the SAM Simulation Core (SSC), which is part of both SAM and the SAM SDK. SAM is a user-friedly desktop application for analysis of renewable energy projects. The SAM SDK (Software Development Kit) is for developers writing their own renewable energy analysis software based on SSC. This manual is written for users of both SAM and the SAM SDK wanting to learn more about the details of SAM's photovoltaic model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Calcined Waste Storage at the Idaho Nuclear Technology and Engineering Center

This comprehensive report provides definitive information regarding the volume, mass, and composition (chemical and radioactivity) of the calcined waste stored at the Idaho Nuclear Technology and Engineering Center on the Idaho National Laboratory Site. The calcine composition data are required for current regulatory compliance and safe operation of the INTEC’s six Calcined Solids Storage Facilities (CSSFs), as well as future retrieval, treatment, and shipping the calcine to an off-Site facility (such as a geologic repository). This report also contains a description of the calcine storage bins that compose each of the CSSFs, emphasizing the features that may be of concern to future calcine retrieval. The CSSFs were designed by different architectural engineering firms and built at different times. Each CSSF has a unique design, reflecting varying design criteria and lessons learned from historical CSSF operation. The varying CSSF designs will affect future calcine retrieval processes and equipment. This revision (5) of the report presents refinements of the databases used to calculate the volume, mass, chemical content, and radioactivity of calcined waste produced and stored within the CSSFs. This revision corrects minor errors and inconsistencies noted in an extensive validation and verification effort of the prior version of the Microsoft Excel workbooks upon which the calcine composition described in this report is based. Historical calcine samples are insufficient in number and scope of analysis to fully characterize the entire inventory of calcine in the CSSFs. Sample data exist for all of the liquid wastes that were calcined. This report provides calcine composition data based on liquid waste sample analyses, the volume of liquid waste calcined, calciner operating data, and CSSF operating data using several large Microsoft Excel (Microsoft 2010) databases and spreadsheets. The Excel spreadsheets are collectively called the Historical Processing Model (HPM). The calcine composition determined by this method compares favorably with historical calcine sample data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Santa Barbara Desalination Digital Twin Technical Report

A digital twin is a computer model that has sufficient breadth and fidelity to enable diagnostic and predictive analyses for outputs of interest. We have constructed a digital twin of the SB desal plant using the ProteusLib software developed for NAWI under task 4.2.2, and specifically the ProteusLib models of pumps, filters, mixers, splitters, and reverse osmosis units and a seawater property model. The steps needed to configure this digital twin with the real conditions of the SB desal plant are: (1) Data reconciliation, which is gathering the data and plant connectivity and ensure that all relevant values are present and in the correct units and validating the data using material and energy balances, and correct for errors, gaps, or deviations; (2) Parameter estimation, which is matching model parameters with data from previous step, filling missing parameters with initial estimates, then iteratively refining estimated values. Once we completed the digital twin, we used the optimization capabilities of ProteusLib to compute outputs of interest over a chosen range of inputs. The primary output of interest to the owners of the plant is its specific energy consumption (kWh/acre-foot). The remainder of this report will provide more detail on the data reconciliation, parameter estimation, and analyses we performed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

2021 Radionuclide Air Emissions Report for Los Alamos National Laboratory, Revised October 2022

Provided is a revision to the Calendar Year 2021 Radionuclide Air Emissions Report for the Los Alamos National Laboratory (LANL). This report is a regulatory compliance deliverable for LANL under the Radionuclide NESHAP, 40 CFR 61 Subpart H. The revised report is needed to correct an error identified regarding generation of wind data files. These wind data files are used for plume modeling as part of calculating off-sites doses from monitored radionuclide air emissions sources. The error resulted from a software update by LANL’s meteorology team after new towers were added to the tower network. As a result of this error, incorrect wind frequencies were assigned to the different stability classes. This slightly affected the plume model calculations and subsequent off-site dose determinations for analyses using these wind files.

2021 Radionuclide, Air Emissions↗
DOE OSTI2024

Elimination LArTPC Simulation Uncertainty

Liquid Argon Time Projection Chambers (LArTPC) are essential for detecting muons and neutrinos by capturing electrons released during particle collisions, which drift toward wire planes under an electric field and induce currents measured to reconstruct particle paths. However, LArTPCs face challenges from effects such as electron-ion recombination, electron diffusion, and electron attenuation, complicating data simulation. The Short Baseline Neutrino (SNB) detector aims to measure neutrinos before oscillation occurs. To bridge the gap between simulation and actual data, we propose modifying the amplitude and width of signals on the TPC wires, addressing uncertainties by adjusting signal characteristics to better match observed data. A Gaussian fit to current waveforms produces hits with associated charge and width, and by comparing data and simulated values, discrepancies highlight areas where the model fails. Initial results indicate the current modification algorithm may increase divergence between simulation and data, necessitating further refinement. A discovered bug in the WireModMakeHist_plug.cpp file, which incorrectly computed simulation and data ratios, underscores the need for precise algorithm adjustments. Future work involves correcting code errors, fine-tuning the model, and conducting multiple simulation runs to enhance statistical confidence and reduce uncertainties, ultimately aiming for accurate LArTPC operation and reliable neutrino detection.

Mkrtchyan, Ka'ren↗
DOE OSTI2015

Calculation of BSM Kaon B-parameters using improved staggered quarks in N_f = 2+1 QCD

We present updated results for kaon B-parameters for operators arising in models of new physics.We use HYP-smeared staggered quarks on the N f = 2 + 1 MILC asqtad lattices. During the last year we have added new ensembles, which has necessitated chiral-continuum fitting with more elaborate fitting functions. We have also corrected an error in a two-loop anomalous dimension used to evolve results between different scales. Our results for the beyond-the-Standard-Model B-parameters have total errors of 5–10%. We find that the discrepancy observed last year between our results and those of the RBC/UKQCD and ETM collaborations for some of the B-parameters has been reduced from 4–5σ to 2–3σ.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernovae in the LSST Era

We perform a rigorous cosmology analysis on simulated Type Ia supernovae (SNe Ia) and evaluate the improvement from including photometric host galaxy redshifts compared to using only the "z spec " subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (~50 deg 2 ) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in combination with a low-z sample based on Data Challenge2. The analysis includes light-curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo-z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the z spec subset, including events with SN+host photo-z results in (i) more precise distances for z > 0.5, (ii) a Hubble diagram that extends 0.3 further in redshift, and (iii) a 50% increase in the Dark Energy Task Force figure of merit (FoM) based on the w 0 w a CDM model. Analyzing 25 simulated data samples, the average bias on w 0 and w a is consistent with zero. The host photo-z systematic of 0.01 reduces FoM by only 2% because (i) most z < 0.5 events are in the z spec subset, (ii) the combined SN+host photo-z has ×2 smaller bias, and (iii) the anticorrelation between fitted redshift and color self-corrects distance errors. To prepare for analyzing real data, the next SN Ia cosmology analysis with photo-zs should include non–SN Ia contamination and host galaxy misassociations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Matlab Based LOCO

The LOCO algorithm has been used by many accelerators around the world. Although the uses for LOCO vary, the most common use has been to find calibration errors and correct the optics functions. The light source community in particular has made extensive use of the LOCO algorithms to tightly control the beta function and coupling. Maintaining high quality beam parameters requires constant attention so a relatively large effort was put into software development for the LOCO application. The LOCO code was originally written in FORTRAN. This code worked fine but it was somewhat awkward to use. For instance, the FORTRAN code itself did not calculate the model response matrix. It required a separate modeling code such as MAD to calculate the model matrix then one manually loads the data into the LOCO code. As the number of people interested in LOCO grew, it required making it easier to use. The decision to port LOCO to Matlab was relatively easy. It's best to use a matrix programming language with good graphics capability; Matlab was also being used for high level machine control; and the accelerator modeling code AT, [5], was already developed for Matlab. Since LOCO requires collecting and processing a relative large amount of data, it is very helpful to have the LOCO code compatible with the high level machine control, [3]. A number of new features were added while porting the code from FORTRAN and new methods continue to evolve, [7][9]. Although Matlab LOCO was written with AT as the underlying tracking code, a mechanism to connect to other modeling codes has been provided.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Permanent Magnet Dipole-Quadrupole and Correcto-Tuner for Small Bore Accelerators

Electromagnets are used to bend electron beams and generate X-ray light. Permanent magnets have many advantages over electromagnets and are being studied as an alternative for small bore accelerators. This paper focuses on the mechanical design of two holders. First, a holder for the corrector and tuner permanent magnet assembly, which aims to correct the errors in the permanent magnetic field strength and orientation. Second, a holder for the dipole-quadrupole magnet assembly, which aims to provide the required magnetic strength to steer the electrons. Prototypes for the corrector, tuner and dipole-quadrupole holders were designed and tested to validate conceptual design. Results show that the corrector magnets can be oriented into a prescribed configuration, the tuner magnets can be oriented simultaneously regardless of the magnetic forces involved, and the dipole-quadrupole magnets can provide the magnetic strength required to steer electrons.

Huschke, Christopher↗
DOE OSTI2020

Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.

Stinis, Panagiotis↗
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↗
DOE OSTI2024

The Single Event Error (SEE) test and analysis of the CMS Endcap Timing Layer readout chip

The ETROC2, the first full size and full functionality prototype chip for the CMS Endcap Timing Layer readout, is strategically designed to meet the SEE immunity requirements of detector operation with the low power constraint. The triplicated periphery and pixel I2C configuration registers are designed with self-correction feature. The pixel readout control is centralized in the global readout and fully triplicated. The pixel readout is not triplicated, instead protected with power-efficient one-bit correction Hamming code. The TMR protection of the on-pixel threshold calibration can be turned off allowing the detection of the beam spot during the beam test by checking the bit-flips of the internal memory cells. In the initial proton beam test in January 2024, the chip readout process did not hang throughout the tests. The Hamming code correction strategy works because the error corrected TDC data were observed in the data frames. The error-injection simulation is performed to analy ze the small number of bit-flips in the configuration registers. We also performed SEE testing with a heavy ion beam in April and the data analysis is ongoing. The detailed design on the SEE immunity and the testing as well as simulation results will be presented, including follow-up SEE testing results in May and June 2024.

Gong, Datao↗
DOE OSTI2021

Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches

Focal Area(s): This white paper addresses how explainable machine learning (ML) algorithms can improve insights gained from complex data (Focal Area 3). We will also address how ML approaches related to sensor compression and low energy AI hardware can be used for efficient data acquisition (Focal Area 1). Science Challenge: We will focus on the coupled water cycle and carbon cycle processes in terrestrial ecosystems and terrestrial-aquatic interfaces. Thus, our approaches will be centered around several data-model integration challenges indicated in EESD strategic plans for two science focus areas: Terrestrial Ecosystem Science and Hydrobiogeochemistry. We can use AI to correct systematic model errors due to biases in either data or model structure/processes. Error patterns in model predictions usually vary by region, model, season etc. But there are systematic patterns in them. e.g., soil moisture tends to be overestimated in the arid western continental United States underestimated in wetter eastern USA; some land surface models tend to underestimate moisture in wet seasons and overestimate in dry seasons. Moreover, the consequences of extreme events (e.g., drought, extreme flooding, storm surges associated with tropical storms, hurricanes) on carbon cycle processes are not well represented in ecosystem and Earth system models. Redox-sensitive processes (e.g., rapid oxidation/reduction of iron and other redox-sensitive elements in soil microsites subjected to fluctuated hydrology) in terrestrial-aquatic interfaces further challenge model predictions of hot-spots (and hot-moments) due to poor understanding of underlying mechanisms. Thus, AI/ML approaches can be used to learn patterns in the data and model errors and use them to build model equations and correct process-based model errors.

54 ENVIRONMENTAL SCIENCES↗