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Effective use of DXTRAN in MCNP - 25480

The DXTRAN variance reduction method enables Monte Carlo radiation transport calculations to deterministically place particle tracks on spheres surrounding geo-metric regions of interest. For many problems DXTRAN is the best way to transport particles to small distant regions or to angle bias particles exiting collisions. We provide guidance for optimal selection of parameters when using both single and nested DXTRAN spheres in Monte Carlo N-Particle radiation transport code (MCNP) calculations. Our recommendations are based upon both the underlying mathematics of DXTRAN and two demonstration MCNP simulations. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗

First-collision source treatment for ray effect mitigation in discrete-ordinate radiation transport solutions

Deterministic transport codes play a fundamental role in the modelling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the S method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solving them using non-traditional techniques such as ray tracing. One such code capable of doing this is Lawrence Livermore National Laboratory's deterministic transport code ARDRA. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low order of convergence and not being conservative. Therefore, a new method has been developed that traces instead to a set of points on each of a zone's surfaces and computing the currents, before using these to obtain the flux. A comparison between these two ray tracing methods showed significant advantages to the new surface method, including inherent conservation, a higher convergence rate, and an increase in calculable information like leakage. This work performed under the auspices of the U.S. Department of Energy by Lawrence Liver- more National Laboratory under Contract DE-AC52-07NA27344. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Particle Swarm Optimisation for group structure optimization for radiotherapy shielding

Neutron transport simulations are ubiquitous in nuclear engineering because they allow one to model experimental systems and render a model platform for easy perturbation of experimental designs. In addition, simulations allow one to gain experimental insight without actually having to go through the trouble of building a physical experiment. Neutron transport simulations can be stochastic or deterministic based. Stochastic neutron transport simulations are typically simulated using the Monte Carlo method and yield very accurate solutions but are computationally expensive, while deterministic methods are typically faster but can be less accurate. Here we focus on optimizing the accuracy of deterministic neutron transport simulations for radiotherapy simulations. Deterministic neutron transport requires discretization of angle, energy, and space to appropriately analyze the system one is trying to model. Discretization of energy is challenging because of the highly variable neutron flux at certain neutron energies. Improper discretization of energy in the transport model can lead to erroneous results and therefore inaccurate interpretations of the solution. In this study, we evaluate Particle Swarm Optimization (PSO) as a mechanism for selecting optimal group structures for radiotherapy shielding. We tested the particle swarm optimization algorithm on radiotherapy shielding problems using Los Alamos National Laboratory's (LANL) main deterministic transport code PARTISN. Results show that the optimized energy group structures generated from the optimization algorithm outperformed LANL's standard energy group structures, and therefore demonstrate utility in using PSO to expedite computation times due to the increased accuracy obtained with a smaller but optimized group structure. (authors)

43 PARTICLE ACCELERATORS↗

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within $1\sigma$. Our model can be used to provide cosmological constraints from forthcoming strong lens surveys, such as the 4MOST Strong Lensing Spectroscopic Legacy Survey (4SLSLS), which is expected to observe 10,000 strong lenses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within 1$\sigma$. Our model can be used to provide cosmological constraints from forthcoming strong lens surveys, such as the 4MOST Strong Lensing Spectroscopic Legacy Survey (4SLSLS), which is expected to observe 10,000 strong lenses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A Workflow to Optimize Fast Neutron Irradiation in A Thermal Neutron Spectrum Test Reactor Leveraging Open-Source Tools

The Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) is one of the key nuclear engineering research and testing facilities within the US Department of Energy (DOE). The ATR is one of few high-power research reactors in the world with different application including accelerated testing of nuclear fuel, materials irradiation in a very high neutron flux environment, and medical radioisotope production [1]. Also, the ATR offers opportunities for testing fast spectrum fission and fusion reactor materials. The key challenges in this area are in further detailing and optimizing a fast spectrum environment within a thermal test reactor. This challenge involves researching, developing, and testing novel concepts for the multiplying of neutron populations into ever higher energy spectra in high flux test reactors like ATR. The main objective of this work is to investigate candidate materials for establishing a fast neutron experiment irradiation in thermal neutron spectrum test reactors which can be accomplished by filtering thermal and epithermal neutrons and boosting fast neutrons at designated irradiation positions. However, adding these filters will render the neutron spectrum and the criticality of the system. The selection of the thickness and material layers should be accomplished by developing an optimization design algorithm that is applicable for ATR to enhance the fast neutron spectrum irradiation utilizing high-fidelity Monte Carlo methods along with advanced machine learning capabilities. This paper presents workflow for design optimization to enhance fast neutron irradiation in the ATR. The workflow leverages open-source tools to develop an algorithm that is viable to ATR and can be leveraged in other reactors. The following sections discuss the development of the experiment design optimization workflow and its application to ATR irradiation positions.

42 - ENGINEERING↗

Neural-network quantum states for the nuclear many-body problem

A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.

Lovato, Alessandro [Argonne; TIFPA-INFN, Trento; V↗

Kinetic Plasma Simulation Capabilities in the MOOSE Framework: Verification of Particle-Particle Collisions

High-fidelity simulations of complex plasma systems allow researchers to gain key insights into and understanding of these systems. To facilitate massively parallel high-fidelity plasma simulations, finite-element-based particle-in-cell capabilities are being developed within the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) based framework called Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER). While SALAMANDER’s primary objective is modeling edge plasmas and plasma-facing components in fusion devices, the particle-in-cell capabilities being developed are general and will support modeling low-temperature plasmas as well. Previously, collisionless magnetostatic simulation capabilities have been verified with the two-stream and Dorey-Guest-Harris instabilities, and single particle motion. Collisions were implemented using the direct simulation Monte Carlo method, and verification of this capability will be presented here several verification problems: relaxation of a randomly initialized gas to a Maxwellian distribution, Fourier heat flow, and comparison of reaction rates to both analytic calculations and those calculated using a multi-term Boltzmann solver.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Dynamical tuning of the chemical potential to achieve a target particle number in grand canonical Monte Carlo simulations

We present a method to facilitate Monte Carlo simulations in the grand canonical ensemble given a target mean particle number. The method imposes a fictitious dynamics on the chemical potential, to be run concurrently with the Monte Carlo sampling of the physical system. Corrections to the chemical potential are made according to time-averaged estimates of the mean and variance of the particle number, with the latter being proportional to thermodynamic compressibility. Here, we perform a variety of tests, and in all cases find rapid convergence of the chemical potential—inexactness of the tuning algorithm contributes only a minor part of the total measurement error for realistic simulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

MO-E-18C-02: Hands-On Monte Carlo Project Assignment as a Method to Teach Radiation Physics

Purpose: To present a hands-on project on Monte Carlo methods (MC) recently added to the curriculum and to discuss the students' appreciation. Methods: Since 2012, a 1.5 hour lecture dedicated to MC fundamentals follows the detailed presentation of photon and electron interactions. Students also program all sampling steps (interaction length and type, scattering angle, energy deposit) of a MC photon transport code. A handout structured in a step-by-step fashion guides student in conducting consistency checks. For extra points, students can code a fully working MC simulation, that simulates a dose distribution for 50 keV photons. A kerma approximation to dose deposition is assumed. A survey was conducted to which 10 out of the 14 attending students responded. It compared MC knowledge prior to and after the project, questioned the usefulness of radiation physics teaching through MC and surveyed possible project improvements. Results: According to the survey, 76% of students had no or a basic knowledge of MC methods before the class and 65% estimate to have a good to very good understanding of MC methods after attending the class. 80% of students feel that the MC project helped them significantly to understand simulations of dose distributions. On average, students dedicated 12.5 hours to the project and appreciated the balance between hand-holding and questions/implications. Conclusion: A lecture on MC methods with a hands-on MC programming project requiring about 14 hours was added to the graduate study curriculum since 2012. MC methods produce “gold standard” dose distributions and slowly enter routine clinical work and a fundamental understanding of MC methods should be a requirement for future students. Overall, the lecture and project helped students relate crosssections to dose depositions and presented numerical sampling methods behind the simulation of these dose distributions. Research funding from governments of Canada and Quebec. PP acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)

07 ISOTOPE AND RADIATION SOURCES↗

SU-F-T-657: In-Room Neutron Dose From High Energy Photon Beams

Purpose: To estimate neutron dose inside the treatment room from photodisintegration events in high energy photon beams using Monte Carlo simulations and experimental measurements. Methods: The Monte Carlo code MCNP6 was used for the simulations. An Eberline ESP-1 Smart Portable Neutron Detector was used to measure neutron dose. A water phantom was centered at isocenter on the treatment couch, and the detector was placed near the phantom. A Varian 2100EX linear accelerator delivered an 18MV open field photon beam to the phantom at 400MU/min, and a camera captured the detector readings. The experimental setup was modeled in the Monte Carlo simulation. The source was modeled for two extreme cases: a) hemispherical photon source emitting from the target and b) cone source with an angle of the primary collimator cone. The model includes the target, primary collimator, flattening filter, secondary collimators, water phantom, detector and concrete walls. Energy deposition tallies were measured for neutrons in the detector and for photons at the center of the phantom. Results: For an 18MV beam with an open 10cm by 10cm field and the gantry at 180°, the Monte Carlo simulations predict the neutron dose in the detector to be 0.11% of the photon dose in the water phantom for case a) and 0.01% for case b). The measured neutron dose is 0.04% of the photon dose. Considering the range of neutron dose predicted by Monte Carlo simulations, the calculated results are in good agreement with measurements. Conclusion: We calculated in-room neutron dose by using Monte Carlo techniques, and the predicted neutron dose is confirmed by experimental measurements. If we remodel the source as an electron beam hitting the target for a more accurate representation of the bremsstrahlung fluence, it is feasible that the Monte Carlo simulations can be used to help in shielding designs.

60 APPLIED LIFE SCIENCES↗

Modification and validation of an analytical source model for external beam radiotherapy Monte Carlo dose calculations

Purpose: A dose calculation tool, which combines the accuracy of the dose planning method (DPM) Monte Carlo code and the versatility of a practical analytical multisource model, which was previously reported has been improved and validated for the Varian 6 and 10 MV linear accelerators (linacs). The calculation tool can be used to calculate doses in advanced clinical application studies. One shortcoming of current clinical trials that report dose from patient plans is the lack of a standardized dose calculation methodology. Because commercial treatment planning systems (TPSs) have their own dose calculation algorithms and the clinical trial participant who uses these systems is responsible for commissioning the beam model, variation exists in the reported calculated dose distributions. Today’s modern linac is manufactured to tight specifications so that variability within a linac model is quite low. The expectation is that a single dose calculation tool for a specific linac model can be used to accurately recalculate dose from patient plans that have been submitted to the clinical trial community from any institution. The calculation tool would provide for a more meaningful outcome analysis. Methods: The analytical source model was described by a primary point source, a secondary extra-focal source, and a contaminant electron source. Off-axis energy softening and fluence effects were also included. The additions of hyperbolic functions have been incorporated into the model to correct for the changes in output and in electron contamination with field size. A multileaf collimator (MLC) model is included to facilitate phantom and patient dose calculations. An offset to the MLC leaf positions was used to correct for the rudimentary assumed primary point source. Results: Dose calculations of the depth dose and profiles for field sizes 4 × 4 to 40 × 40 cm agree with measurement within 2% of the maximum dose or 2 mm distance to agreement (DTA) for 95% of the data points tested. The model was capable of predicting the depth of the maximum dose within 1 mm. Anthropomorphic phantom benchmark testing of modulated and patterned MLCs treatment plans showed agreement to measurement within 3% in target regions using thermoluminescent dosimeters (TLD). Using radiochromic film normalized to TLD, a gamma criteria of 3% of maximum dose and 2 mm DTA was applied with a pass rate of least 85% in the high dose, high gradient, and low dose regions. Finally, recalculations of patient plans using DPM showed good agreement relative to a commercial TPS when comparing dose volume histograms and 2D dose distributions. Conclusions: A unique analytical source model coupled to the dose planning method Monte Carlo dose calculation code has been modified and validated using basic beam data and anthropomorphic phantom measurement. While this tool can be applied in general use for a particular linac model, specifically it was developed to provide a singular methodology to independently assess treatment plan dose distributions from those clinical institutions participating in National Cancer Institute trials.

60 APPLIED LIFE SCIENCES↗

Nuclear energy density functionals grounded in ab initio calculations

Here, we discuss the construction of a nuclear energy density functional (EDF) from ab initio computations and advocate the need for a methodical approach that is free from ad hoc assumptions. The equations of state (EoSs) of symmetric nuclear and pure neutron matter are computed using the chiral NNLO sat and the phenomenological AV4' + UIX c Hamiltonians as inputs to self-consistent Green's function (SCGF) and auxiliary field diffusion Monte Carlo (AFDMC) methods. We propose a convenient parametrization of the EoS as a function of the Fermi momentum and fit it on the SCGF and AFDMC calculations. We apply the ab initio based EDF to carry out an analysis of the binding energies and charge radii of different nuclei in the local density approximation. The NNLO sat -based EDF produces encouraging results, whereas the AV4' + UIX c -based one is farther from experiment. Possible explanations of these different behaviors are suggested, and the importance of gradient and spin-orbit terms is analyzed. Our paper paves the way for a practical and systematic way to merge ab initio nuclear theory and density functional theory, while shedding light on some critical aspects of this procedure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A Monte Carlo investigation of low-Z target image quality generated in a linear accelerator using Varian's VirtuaLinac

Purpose: The focus of this work was the demonstration and validation of VirtuaLinac with clinical photon beams and to investigate the implementation of low-Z targets in a TrueBeam linear accelerator (Linac) using Monte Carlo modeling. Methods: VirtuaLinac, a cloud based web application utilizing Geant4 Monte Carlo code, was used to model the Linac treatment head components. Particles were propagated through the lower portion of the treatment head using BEAMnrc. Dose distributions and spectral distributions were calculated using DOSXYZnrc and BEAMdp, respectively. For validation, 6 MV flattened and flattening filter free (FFF) photon beams were generated and compared to measurement for square fields, 10 and 40 cm wide and at d{sub max} for diagonal profiles. Two low-Z targets were investigated: a 2.35 MeV carbon target and the proposed 2.50 MeV commercial imaging target for the TrueBeam platform. A 2.35 MeV carbon target was also simulated in a 2100EX Clinac using BEAMnrc. Contrast simulations were made by scoring the dose in the phosphor layer of an IDU20 aSi detector after propagating through a 4 or 20 cm thick phantom composed of water and ICRP bone. Results: Measured and modeled depth dose curves for 6 MV flattened and FFF beams agree within 1% for 98.3% of points at depths greater than 0.85 cm. Ninety three percent or greater of points analyzed for the diagonal profiles had a gamma value less than one for the criteria of 1.5 mm and 1.5%. The two low-Z target photon spectra produced in TrueBeam are harder than that from the carbon target in the Clinac. Percent dose at depth 10 cm is greater by 3.6% and 8.9%; the fraction of photons in the diagnostic energy range (25–150 keV) is lower by 10% and 28%; and contrasts are lower by factors of 1.1 and 1.4 (4 cm thick phantom) and 1.03 and 1.4 (20 cm thick phantom), for the TrueBeam 2.35 MV/carbon and commercial imaging beams, respectively. Conclusions: VirtuaLinac is a promising new tool for Monte Carlo modeling of novel target designs. A significant spectral difference is observed between the low-Z target beam on the Clinac platform and the proposed imaging beam line on TrueBeam, with the former providing greater diagnostic energy content.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

WE-AB-209-01: A Monte Carlo-Based Method to Include Random Errors in Robust Optimization

Purpose: To develop an efficient method to implement random set-up errors and organ motion in robust optimization for proton therapy treatment planning. Methods: The plans were created with an in-house robust optimizer, coupled with a super-fast Monte Carlo (MC) engine (less than 1 minute for final dose). MC simulates random errors by shifting randomly the starting point of each particle, according to their probability distribution. Such strategy assumes a sufficient number of treatment fractions. Two strategies are presented: 1) Full robust optimization with beamlets that already include the effect of random errors and 2) Mixed robust optimization, where the nominal beamlets are involved but a correction term C modifies the prescription. Starting from C=0, the method alternates optimization of the spot weights with the nominal beamlets and updates of C, with C=Drandom-Dnominal and where Drandom results from a regular MC computation (without pre-computed beamlets) that simulates random errors. Updates of C can be triggered as often as necessary by running the MC engine with the last corrected values for the spot weights as input. The method was applied to lung and prostate cases. For both patients the range error was set to 3%, systematic setup error to 5mm and standard deviation for random errors to 5 mm. Comparison between full robust optimization and the mixed strategy (with 3 updates of C) is presented. Results: Target coverage was far below the clinical constraints (D{sub 95} > 95% of the prescribed dose) for plans where random errors were not simulated, especially for lung case. However, by using the proposed strategies the plans achieved a target coverage above clinical constraints. Conclusion: Full robust optimization gives better results than the mixed strategy, but the latter can be useful in cases where a MC engine is not available or too computationally intensive for beamlets calculation.

60 APPLIED LIFE SCIENCES↗

PIC simulation of electrodeless plasma thruster with rotating electric field

For longer lifetime of electric propulsion system, an electrodeless plasma thruster with rotating electric field have been proposed utilizing a helicon plasma source. The rotating electric field may produce so-called Lissajous acceleration of helicon plasma in the presence of diverging magnetic field through a complicated mechanism originating from many parameters. Two-dimensional simulations of the Lissajous acceleration were conducted by a code based on Particle-In-Cell (PIC) method and Monte Carlo Collision (MCC) method for understanding plasma motion in acceleration area and for finding the optimal condition. Obtained results show that azimuthal current depends on ratio of electron drift radius to plasma region length, AC frequency, and axial magnetic field. When ratio of cyclotron frequency to the AC frequency is higher than unity, reduction of the azimuthal current by collision effect is little or nothing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Thermal diffusion segregation of an impurity in a driven granular fluid

We study segregation of an impurity in a driven granular fluid under two types of steady states. In the first state, the granular gas is driven by a stochastic volume force field with a Fourier-type profile while in the second state, the granular gas is sheared in such a way that inelastic cooling is balanced by viscous heating. We compare theoretical results derived from a solution of the (inelastic) Boltzmann equation at Navier-Stokes (NS) order with those obtained from the Direct Monte Carlo simulation (DSMC) method and molecular dynamics (MD) simulations. Good agreement is found between theory and simulation, which provides strong evidence of the reliability of NS granular hydrodynamics for these steady states (including the dynamics of the impurity), even at high inelasticity. In addition, preliminary results for thermal diffusion in granular fluids at moderate densities are also presented. As for dilute gases, excellent agreement is also found in this more general case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗