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DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Quantifying the role of moss in terrestrial ecosystem carbon dynamics in northern high latitudes

Mosses are ubiquitous in northern terrestrial ecosystems, and play an important role in regional carbon, water and energy cycling. Current global land surface models that do not consider mosses may bias the quantification of regional carbon dynamics. Here we incorporate mosses as a new plant functional type into the process-based Terrestrial Ecosystem Model (TEM 5.0), to develop a new model (TEM_Moss). The new model explicitly quantifies the interactions between vascular plants and mosses and their competition for energy, water, and nutrients. Compared to the estimates using TEM 5.0, the new model estimates that the regional terrestrial soils currently store 132.7 Pg more C and will store 157.5 and 179.1 Pg more C under the RCP8.5 and RCP2.6 scenarios, respectively, by the end of the 21st century. Ensemble regional simulations forced with different parameters for the 21st century with TEM_Moss predict that the region will accumulate 161.1±142.1 Pg C under the RCP2.6 scenario and 186.7±166.1 Pg C under the RCP8.5 scenario over the century. Our study highlights the necessity of coupling moss into Earth system models to adequately quantify terrestrial carbon–climate feedbacks in the Arctic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

N-Terminal Decarboxylation as a Probe for Intramolecular Contact Formation in $γ$-Glu-(Pro) n -Met Peptides

The kinetics of intramolecular-contact formation between remote functional groups in peptides with restricted conformational flexibility were examined using designed peptides with variable-length proline bridges. As probes for this motion, free radicals were produced using the • OH-induced oxidation at the C-terminal methionine residue of γ-Glu-(Pro) n -Met peptides ( n = 0–3). The progress of the radicals’ motion along the proline bridges was monitored as the radicals underwent reactions along the peptides’ backbones. Of particular interest was the reaction between the sulfur atom located in the side chain of the oxidized Met residue and the unprotonated amino group of the glutamic acid moiety. Interactions between them were probed by the radiation-chemical yields (expressed as G values) of the formation of C-centered, α-aminoalkyl radicals (αN) on the Glu residue. These radicals were monitored directly or via their reaction with p -nitroacetophenone (PNAP) to generate the optically detected PNAP •– radical anions. The yields of these αN radicals were found to be linearly dependent on the number of Pro residues. A constant decrease by 0.09 μM J –1 per spacing Pro residue of the radiation-chemical yields of G (αN) was observed. Previous reports support the conclusion that the αN radicals in these cases would have to result from (S$\therefore$N) + -bonded cyclic radical cations that arose as a result from direct contact between the ends of the peptides. Furthermore, by analogy with the rate constants for the formation of intramolecularly (S$\therefore$S) + -bonded radical cations in Met-(Pro) n -Met peptides ( J. Phys. Chem. B 2016 , 120 , 9732), the rate constants for the formation of intramolecularly (S$\therefore$N) + -bonded radical cations are activated to the same extent for all of the γ-Glu-(Pro) n -Met peptides. Thus, the continuous decrease of G (αN) with the number of Pro residues (from 0 to 3) suggests that the formation of a contact between the S-atom in the C-terminal Met residue and the N-atom of a deprotonated N-terminal amino group of Glu is controlled in peptides with 0 to 3 Pro residues by the relative diffusion of the S •+ and unoxidized N-atom. The overall rate constants of cyclization to form the (S$\therefore$N)-bonded radical cations were estimated to be 3.8 × 10 6 , 1.8 × 10 6 , and 8.1 × 10 5 s –1 for peptides with n = 0, 1, and 2 Pro residues, respectively. If activation is the same for all of the peptides, then these rate constants are a direct indication for the end-to-end dynamics along the chain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Constitutive Modeling of the Flow Stress of GCr15 Continuous Casting Bloom in the Heavy Reduction Process

According to the calculation results of a 3D thermomechanical-coupled finite-element (FE) model of GCr15 bearing steel bloom during a heavy reduction (HR) process, the variation ranges in the strain rate and strain under HR were described. In addition, the hot deformation behavior of the GCr15 bearing steel was studied over the temperature range from 1023 K to 1573 K (750 °C to 1300 °C) with strain rates of 0.001, 0.01, and 0.1 s{sup −1} in single-pass thermosimulation compression experiments. To ensure the accuracy of the constitutive model, the temperature range was divided into two temperature intervals according to the fully austenitic temperature of GCr15 steel [1173 K (900 °C)]. Two sets of material parameters for the constitutive model were derived based on the true stress–strain curves of the two temperature intervals. A flow stress constitutive model was established using a revised Arrhenius-type constitutive equation, which considers the relationships among the material parameters and true strain. This equation describes dynamic softening during hot compression processes. Considering the effect of glide and climb on the deformation mechanism, the Arrhenius-type constitutive equation was modified by a physically based approach. This model is the most accurate over the temperatures ranging from 1173 K to 1573 K (900 °C to 1300 °C) under HR deformation conditions (ignoring the range from 1273 K to 1573 K (1000 °C to 1300 °C) with a strain rate of 0.1 s{sup −1}). To ensure the convergence of the FE calculation, an approximated method was used to estimate the flow stress at temperatures greater than 1573 K (1300 °C).

36 MATERIALS SCIENCE↗

DOE OSTI2021

Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Ice Ih, the common form of ice in the biosphere, contains proton disorder. Its proton-ordered counterpart, ice XI, is thermodynamically stable below 72 K. However, the formation of ice XI is kinetically hindered, and experimentally it is obtained by doping with KOH. Doping creates ionic defects that promote the migration of protons and the associated change in proton configuration. In this article, we mimic the effect of doping with a bias potential that enhances the formation of ionic defects in molecular dynamics simulations. The recombination of the ions thus formed proceeds through fast migration of the hydroxide along hydrogen bond loops, providing a physical and expedite way to change the proton configuration. Here, a key ingredient of this approach is a machine learning potential trained with density functional theory data and capable of modelling molecular dissociation. We exemplify the usefulness of this idea by studying the order-disorder transition using an appropriate order parameter that distinguishes the proton environments in ice Ih and XI. We calculate the changes in free energy, enthalpy, and entropy associated with the transition. Our estimated entropy agrees with experiment within the error bars of the calculation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

Abstract We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established analytically.

Evangelou, Nikolaos↗

DOE OSTI2024

A Time-Domain Protection Approach for AC Transmission Systems With Grid-Forming Resources

Ac transmission protection must reliably detect, classify, and locate short-circuit faults from voltage and current measurements. At present, these functionalities, which have been classically engineered using phasors approaches, are being challenged by the dynamic behavior and fault-current limits of converter-based generation. This paper tackles these challenges by engineering a time-domain protection approach that leverages the classical Bergeron model in a new manner. Low- and high-impedance faults are detected and classified by ascertaining how well line voltage and current measurements match the Bergeron equations. Faults are located by posing a novel one-variable optimization problem, whereas voltage and current waveforms at the fault location are estimated by unveiling rigorous relationships. The proposed elements are secure against external faults, measurement errors, and variation of line parameters and sampling time. Furthermore, these advances are tested via electromagnetic transient simulations and are significant to satisfy IEEE and North American Electric Reliability Corporation requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

pyTCR: A tropical cyclone rainfall model for python

pyTCR is a climatology software package developed in the Python programming language. It integrates the capabilities of several legacy physical models and increases computational efficiency to allow rapid estimation of tropical cyclone (TC) rainfall consistent with the large-scale environment. Specifically, pyTCR implements a horizontally distributed and vertically integrated model [Zhu et al., 2013] for simulating rainfall driven by TCs. Along storm tracks, rainfall is estimated by computing the cross-boundary-layer, upward water vapor transport caused by different mechanisms including frictional convergence, vortex stretching, large-scale baroclinic effect (i.e., wind shear), topographic forcing, and radiative cooling [Lu et al., 2018]. The package provides essential functionalities for modeling and interpreting spatio-temporal TC rainfall data. pyTCR requires a limited number of model input parameters, making it a convenient and useful tool for analyzing rainfall mechanisms driven by TCs. To sample rare (most intense) rainfall events that are often of great societal interest, pyTCR adapts and leverages outputs from a statistical-dynamical TC downscaling model [Lin et al., 2023] capable of rapidly generating a large number of synthetic TCs given a certain climate. As a result, pyTCR significantly reduces computational effort and improves the efficiency in capturing extreme TC rainfall events at the tail of the distributions from limited datasets. Furthermore, the TC downscaling model is forced entirely by large-scale environmental conditions from reanalysis data or coupled General Circulation Models (GCMs), simplifying the projection of TC-induced rainfall and wind speed under future climate using pyTCR. Finally, pyTCR can be coupled with hydrological and wind models to assess risks associated with independent and compound events (e.g., storm surges and freshwater flooding).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Integrated Heat Exchanger-Phase Change Material Thermal Energy Storage System

The purpose of this study is to experimentally investigate the thermal performance of an innovative thermal energy storage (TES) system that combines the advantages of the phase-change material (PCM)/graphite foam latent heat TES medium developed at Argonne National Laboratory (Argonne) and the internally supported plate-fin (ISPE) cell architecture heat transfer fluid (HTF) flow channels developed at Brayton Energy (Brayton). Several essential tasks were accomplished: (1) Thermal property characterization. Thermal properties of the graphite foam were characterized, providing necessary data for experimental result analysis and numerical simulation. (2) Design and optimization of lab-scale test module. Based on Brayton’s full-scale heat exchanger (HX)-TES system, the experimental test module was designed, optimized, and fabricated. (3) Thermal performance testing and data analysis. Five cycle tests were successfully conducted—including one with approximately 3.5 psig of pressure applied to the diaphragms—to investigate the thermal performance of the experimental test module for charging and discharging. Temperature profiles were generated for each charging test and discharging test as a function of time. The temperature profiles clearly show three TES stages: sensible heat (temperature increase), latent heat (melting), and sensible heat (temperature increase) for the charging process. Similarly, the temperature profiles clearly show three thermal energy release stages: sensible heat (temperature decrease), latent heat (solidification), and sensible heat (temperature decrease). Melting and solidification of the PCM generally occurred in relatively narrow temperature ranges, indicated by the flattened temperature regions in the temperature profiles. These phase changes ranged approximately 3°C for melting and 3.5°C for solidification. The charging and discharging temperature profiles were similar for similar experimental parameter tests whether or not pressure was applied to the diaphragm to eliminate the gap between the HX surface and the TES subsystem. This indicates that the effect of a small gap between the HX surface and the TES subsystem is insignificant for charging and discharging. (4) Comparison of experimental data and simulation results. We compared the experimental data to the numerical simulation results. Numerical simulations were conducted by using the ANSYS FLUENT 2019 R3 commercial computational fluid dynamics software. The predicted phase-change times agreed reasonably well with those from the experimental data. In most cases, the estimated time differences between the relative phase changes were within 16%. The predicted start and end times for the charging process agreed well with those from the experimental data. However, the simulation results showed earlier start and end times than the experimental data for the discharging process. Overall, the experimental data and its comparison with the simulation predictions verified the technical viability of the integrated ISPF HX-PCM/graphite foam latent-heat TES system.

25 ENERGY STORAGE↗

DOE OSTI2013

Oak Ridge Bio-surveillance Toolkit (ORBiT): Integrating Big-Data Analytics with Visual Analysis for Public Health Dynamics

In this position paper, we describe the design and implementation of the Oak Ridge Bio-surveillance Toolkit (ORBiT): a collection of novel statistical and machine learning tools implemented for (1) integrating heterogeneous traditional (e.g. emergency room visits, prescription sales data, etc.) and non-traditional (social media such as Twitter and Instagram) data sources, (2) analyzing large-scale datasets and (3) presenting the results from the analytics as a visual interface for the end-user to interact and provide feedback. We present examples of how ORBiT can be used to summarize ex- tremely large-scale datasets effectively and how user interactions can translate into the data analytics process for bio-surveillance. We also present a strategy to estimate parameters relevant to dis- ease spread models from near real time data feeds and show how these estimates can be integrated with disease spread models for large-scale populations. We conclude with a perspective on how integrating data and visual analytics could lead to better forecasting and prediction of disease spread as well as improved awareness of disease susceptible regions.

Ramanathan, Arvind↗

DOE OSTI2024

Photosynthetic and stomatal response, and leaf traits, of two pine species, Talladega National Forest, Alabama, 2023

Photosynthetic response to irradiance (AQ), CO2 (ACi) and stomatal response to irradiance measurements on two pine species common to the southeast United States, long leaf pine, Pinus palustris Mill., and loblolly pine, Pinus taeda L.. Data were collected in June 2023 within the Oakmulgee District of the Talladega National Forest in west-central Alabama at the NEON site (TALL). Measurements were made with four LI-COR LI-6800 and one LI-COR LI-6400XT gas exchange systems. ACi and AQ curves enable the estimation of photosynthetic parameters (e.g., Vcmax, Jmax, Rd, aQY), while stomatal response curves enable the estimation of stomatal slope and intercept parameters. Foliar trait data (needle dimensions, leaf mass per area, leaf nitrogen concentration) is provided for all gas exchange samples. The data package files include multiple raw and processed data files (csv), metadata files (csv), the complete instrument output for all measurements in .zip format, and a pdf describing the experimental protocol.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

CAN A NANOFLARE MODEL OF EXTREME-ULTRAVIOLET IRRADIANCES DESCRIBE THE HEATING OF THE SOLAR CORONA?

Nanoflares, the basic units of impulsive energy release, may produce much of the solar background emission. Extrapolation of the energy frequency distribution of observed microflares, which follows a power law to lower energies, can give an estimation of the importance of nanoflares for heating the solar corona. If the power-law index is greater than 2, then the nanoflare contribution is dominant. We model a time series of extreme-ultraviolet emission radiance as random flares with a power-law exponent of the flare event distribution. The model is based on three key parameters: the flare rate, the flare duration, and the power-law exponent of the flare intensity frequency distribution. We use this model to simulate emission line radiance detected in 171 A, observed by Solar Terrestrial Relation Observatory/Extreme-Ultraviolet Imager and Solar Dynamics Observatory/Atmospheric Imaging Assembly. The observed light curves are matched with simulated light curves using an Artificial Neural Network, and the parameter values are determined across the active region, quiet Sun, and coronal hole. The damping rate of nanoflares is compared with the radiative losses cooling time. The effect of background emission, data cadence, and network sensitivity on the key parameters of the model is studied. Most of the observed light curves have a power-law exponent, {alpha}, greater than the critical value 2. At these sites, nanoflare heating could be significant.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

THE EFFECT OF ABSORPTION SYSTEMS ON COSMIC REIONIZATION

We use large-scale simulations to investigate the morphology of reionization during the final, overlap phase. Our method uses an efficient three-dimensional smoothing technique that takes into account the finite mean free path due to absorption systems, {lambda}{sub abs}, by only smoothing over scales R{sub s} < {lambda}{sub abs}. The large dynamic range of our calculations is necessary to resolve the neutral patches left at the end of reionization within a representative volume; we find that simulation volumes exceeding several hundred Mpc on a side are necessary in order to properly model reionization when the neutral fraction is {approx_equal} 0.01-0.3. Our results indicate a strong dependence of percolation morphology on a large and uncertain region of model parameter space. The single most important parameter is the mean free path to absorption systems, which serve as opaque barriers to ionizing radiation. If these absorption systems were as abundant as some realistic estimates indicate, the spatial structure of the overlap phase is considerably more complex than previously predicted. In view of the lack of constraints on the mean free path at the highest redshifts, current theories that do not include absorption by Lyman-limit systems, and in particular three-dimensional simulations, may underestimate the abundance of neutral clouds at the end of reionization. This affects predictions for the 21 cm signal associated with reionization, interpretation of absorption features in quasar spectra at z {approx} 5-6, the connection between reionization and the local universe, and constraints on the patchiness and duration of reionization from temperature fluctuations measured in the cosmic microwave background arising from the kinetic Sunyaev-Zel'dovich effect.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters (GCs) to giant ellipticals, which was performed in order to investigate the origin of ultracompact dwarf galaxies (UCDs). The data were mostly drawn from Forbes et al. We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light ratios, and estimated ages and metallicities. We completed the sample with GCs of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio, and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies, respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include GCs and UCDs. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies are similar to those of the GCs and UCDs. The brightest low-metallicity GCs and UCDs tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Bayesian calibration of a hybrid nuclear collision model using $\textit{p}$₋Pb and Pb-Pb data at energies available at the CERN Large Hadron Collider

Here, we posit a unified hydrodynamic and microscopic description of the quark-gluon plasma (QGP) produced in ultrarelativistic p₋Pb and Pb-Pb collisions at $\sqrt{s}_\text{NN}$ = 5.02 TeV and evaluate our assertion using Bayesian inference. Specifically, we model the dynamics of both collision systems using initial conditions with parametric nucleon substructure, a preequilibrium free streaming stage, event-by-event viscous hydrodynamics, and a microscopic hadronic afterburner. Free parameters of the model, which describe the initial state and QGP medium are then simultaneously calibrated to fit charged-particle yields, mean $p_T$, and flow cumulants. We argue that the global agreement of the calibrated model with the experimental data strongly supports the existence of hydrodynamic flow in small collision systems at ultrarelativistic energies, and that the flow produced develops at length scales smaller than a single proton. Posterior estimates for the model's input parameters are obtained, and new insights into the temperature dependence of the QGP transport coefficients and event-by-event structure of the proton are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2014

Motion tracking in the liver: Validation of a method based on 4D ultrasound using a nonrigid registration technique

Purpose: Treatments like radiotherapy and focused ultrasound in the abdomen require accurate motion tracking, in order to optimize dosage delivery to the target and minimize damage to critical structures and healthy tissues around the target. 4D ultrasound is a promising modality for motion tracking during such treatments. In this study, the authors evaluate the accuracy of motion tracking in the liver based on deformable registration of 4D ultrasound images. Methods: The offline analysis was performed using a nonrigid registration algorithm that was specifically designed for motion estimation from dynamic imaging data. The method registers the entire 4D image data sequence in a groupwise optimization fashion, thus avoiding a bias toward a specifically chosen reference time point. Three healthy volunteers were scanned over several breathing cycles (12 s) from three different positions and angles on the abdomen; a total of nine 4D scans for the three volunteers. Well-defined anatomic landmarks were manually annotated in all 96 time frames for assessment of the automatic algorithm. The error of the automatic motion estimation method was compared with interobserver variability. The authors also performed experiments to investigate the influence of parameters defining the deformation field flexibility and evaluated how well the method performed with a lower temporal resolution in order to establish the minimum frame rate required for accurate motion estimation. Results: The registration method estimated liver motion with an error of 1 mm (75% percentile over all datasets), which was lower than the interobserver variability of 1.4 mm. The results were only slightly dependent on the degrees of freedom of the deformation model. The registration error increased to 2.8 mm with an eight times lower temporal resolution. Conclusions: The authors conclude that the methodology was able to accurately track the motion of the liver in the 4D ultrasound data. The authors believe that the method has potential in interventions on moving abdominal organs such as MR or ultrasound guided focused ultrasound therapy and radiotherapy, pending the method is enabled to run in real-time. The data and the annotations used for this study are made publicly available for those who would like to test other methods on 4D liver ultrasound data.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-C-BRD-03: Closing the Loop On Virtual IMRT QA

Purpose: To develop an algorithm that predicts a priori IMRT QA passing rates. Methods: 416 IMRT plans from all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam linacs (Varian Medical Systems, Palo Alto, CA). The 3%/3mm and 2%/2mm local distance to agreement (DTA) were recorded during clinical operations using a commercial 2D diode array (MapCHECK 2, Sun Nuclear, Melbourne, FL). Each plan was characterized by 37 metrics that describe different failure modes between the calculated and measured dose. Machine-learning algorithms (MLAs) were trained to learn the relation between the plan characteristics and each passing rate. Minimization of the cross validated error, together with maximum a posteriori estimation (MAP), were used to choose the model parameters. Results: 3%/3mm local DTA can be predicted with an error smaller than 3% for 98% of the plans. For the remaining 2% of plans, the residual error was within 5%. For 2%/2mm local DTA passing rates, 96% percent of the plans were successfully predicted with an error smaller than 5%. All high-risk plans that failed the 2%/2mm local criteria were correctly identified by the algorithm. The most important metric to describe the passing rates was determined to be the MU per Gray (modulation factor). Conclusions: Logs files and independent dose calculations have been suggested as possible substitutes for measurement based IMRT QA. However, none of these methods answer the fundamental question of whether a plan can be delivered with a clinically acceptable error given the limitations of the linacs and the treatment planning system. Predicting the IMRT QA passing rates a priori closes that loop. For additional robustness, virtual IMRT QA can be combined with Linac QA and log file analysis to confirm appropriate delivery.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

TIC 168789840: A Sextuply Eclipsing Sextuple Star System

We report the discovery of a sextuply eclipsing sextuple star system from TESS data, TIC 168789840, also known as TYC 7037-89-1, the first known sextuple system consisting of three eclipsing binaries. The target was observed in Sectors 4 and 5 during Cycle 1, with lightcurves extracted from TESS Full Frame Image data. It was also previously observed by the WASP survey and ASAS-SN. The system consists of three gravitationally bound eclipsing binaries in a hierarchical structure of an inner quadruple system with an outer binary subsystem. Follow-up observations from several different observatories were conducted as a means of determining additional parameters. The system was resolved by speckle interferometry with a 0.″42 separation between the inner quadruple and outer binary, inferring an estimated outer period of ∼2 kyr. It was determined that the fainter of the two resolved components is an 8.217 day eclipsing binary, which orbits the inner quadruple that contains two eclipsing binaries with periods of 1.570 days and 1.306 days. Markov Chain Monte Carlo (MCMC) analysis of the stellar parameters has shown that the three binaries of TIC 168789840 are “triplets,” as each binary is quite similar to the others in terms of mass, radius, and T {sub eff}. As a consequence of its rare composition, structure, and orientation, this object can provide important new insight into the formation, dynamics, and evolution of multiple star systems. Future observations could reveal if the intermediate and outer orbital planes are all aligned with the planes of the three inner eclipsing binaries.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗