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This report explores a novel application of the initial state radiation (radiative return) method in an electron-positron collider, to measure the electron-positron annihilation into a neutral pion and a photon  reaction cross section at low energies. The challenge of using ISR events for analysis is due to the combinatorics issue presented by the extra photon(s) in the final state. Through measuring the cross section, access is gained to the time-like electromagnetic transition form factor (TFF) of the neutral pion. This can be used to constrain theoretical models of the hadronic light-by-light scattering contribution to the anomalous magnetic moment of the muon (AMM). The aim of this project was to determine if existing, or expected, data samples at the experiments KLOE-2 in Frascati and BES-III in Beijing could provide competitive results for the time-like TFF slope parameter. The analysis was performed through the construction of an event generator, where events were generated for three different reaction models. Considering the amount of data available at low energies, this study indicates that the ISR approach could be a viable option to enhance the data sample in the low energy region. The most promising experiment for further analysis is here indicated to be KLOE-2. Compared to tabulated values for the form factor slope parameter, the uncertainty retrieved here is roughly on the same order of magnitude or smaller. / I denna rapport behandlas en ny metod för analys av ISR-data från experiment vid elektron-positron-kolliderare, så som KLOE-2 och BES-III. Strålning i form av en eller flera fotoner som strålats ut från elektronen eller positronen innan kollision kallas ISR. Då en foton strålas ut  från initialtillståndet sänks reaktionens nominella energi. Detta möjliggör analys av reaktioner över ett kontinuerligt energispektrum. Utmaningen med ISR analys ligger i kombinatoriken som uppstår då det återfinns ytterligare fotoner i sluttillståndet för reaktionen.I rapporen beskrivs processen elektron-positron-annihilation till en neutral pion och en foton. Denna reaktion är intressant då kunskap om dess reaktionstvärsnitt ger tillgång till den elektromagnetiska formfaktorn för den neutrala pionen. Formfaktorn beskriver hur reaktionen i fråga avviker från en punkt-lik elektromagnetisk växelverkan. Den elektromagnetiska fromfaktorn för den neutrala pionen är i sin tur en viktig del i beräkningarna för det hadroniska bidraget till myonens anomala magnetiska moment (AMM). Eftersom AMM är experimentellt uppmätt till mycket god noggrannhet kan jämförelser med teoretiska modeller göras med hög precision.  Vid låg reaktionsenergi kan formfaktorn beskrivas med endast en parameter, lutningsparametern. Från Monte Carlo genererad ISR-data har i denna rapport lutningsparametern bestämts med noggrannhet som är likvärdig eller bättre än tabulerade värden, beroende på mängd analyserad data samt val av analysmetod.

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Monte Carlo (MC) methods for radiotherapy dose calculation are widely accepted as capable of achieving high accuracy. In particular, MC calculations have been demonstrated to successfully reproduce measured dose distributions in complex situations where alternative dose calculation algorithms failed (for example, regions of charged particle disequilibrium). For this reason, MC methods are likely to play a central role in radiotherapy dose calculations and dose verification in the future. However, clinical implementations of MC calculations have typically been limited due to the high computational demands. In order to improve the feasibility of using MC simulations clinically, the simulation techniques must be made more efficient. This dissertation presents a number of approaches to improve the efficiency of MC dose calculations. One of the most time consuming parts of source modeling is the simulation of the secondary collimators, which absorb particles to define the rectangular boundaries of radiation fields. The approximation of assuming negligible transmission through and scatter from the secondary collimators was evaluated for accuracy and efficiency using both graphics processing unit (GPU)-based and central processing unit (CPU)-based MC approaches. The new dose calculation engine, gDPM, that utilizes GPUs to perform MC simulations was developed to a state where accuracy comparable to conventional MC algorithms was attained. However, in GPU- based dose calculation, source modeling was found to be an efficiency bottleneck. To address this, a sorted phase-space source model was implemented (the phase-space- let, or PSL model), as well as a hybrid source model where a phase-space source was used only for extra-focal radiation and a point source modeled focal source photons. All of these methods produced results comparable with standard CPU-based MC simulations in minutes, rather than hours, of calculation time. While maintaining reasonable accuracy, the hybrid source model increased source generation time by a factor of ~2-5 when compared with the PSL source model. A variance reduction technique known as photon splitting was also implemented into gDPM, to evaluate its effectiveness at reducing simulation times in GPU calculations. Finally, an alternative CPU-based MC dose calculation technique was presented for specific applications in pre-treatment dose verification. The method avoids the requirement of plan-specific MC simulations. Using measurements from an electronic portal imaging device (EPID), pre-calculated MC beamlets in a spherical water phantom were modulated to obtain a dose reconstruction. / Graduate

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The effects of the microstructure of metal films on electric component performance and longevity have become increasingly important with the recent advances in nanotechnology. Depending on the application of the metal films and interconnects, certain microscopic structures and properties are preferred over others. A common method to produce these films and interconnects is through electrodeposition. As with every process, the ability to control the end product requires a detailed understanding of the system and the effect of operating conditions on the resulting product. To address this problem, a three-dimensional on-lattice kinetic Monte Carlo (KMC) method is developed to conduct atomistic simulations of single crystal and polycrystalline metal electrodeposition. The method utilizes the semi-empirical multi-body embedded-atom method (EAM) potential that accounts for the cohesive forces in a metallic system. The resulting computational method, KMC-EAM, enables highly descriptive simulations of electrodeposition processes to be performed over experimentally relevant scales. In this work, kinetically controlled copper electrodeposition onto single crystal copper under galvanostatic direct-current conditions and polycrystalline copper under potentiostatic direct-current conditions is modelled using the aforementioned KMC method. Four types of surface processes are considered during electrodeposition: deposition, dissolution, surface diffusion and grain boundary diffusion. The equilibrium microstructures from single crystal experiments were validated using molecular dynamics (MD) simulations through the comparison of energy per atom and average coordination number. The growth mode observed is in agreement with experimental results for the same orientation of copper. MD simulation relaxes constraints and approximations resulting from the use of KMC. Results indicate that collective diffusion mechanisms are essential in order to accurately model the evolution of coating morphology during electrodeposition. In the polycrystalline simulations, the effect of surface energy is taken into account in the propensities of deposition and dissolution. Sub-surface grain volume measurements were obtained from simulation results and the grain volume evolution with time is in agreement with both qualitative observations based on the deposit morphology and surface energy calculations. Simulations of polycrystalline deposition agree with findings from experimental studies that the evolution of the root-mean-squared roughness of the deposit during the early stages of deposition follows a power law relationship with respect to time $\approx t^{n}$. Furthermore, the power law exponent on time is determined to be $n \approx 0.5$, also in agreement with the experimental values reported in the literature.

In the field of radiation protection, complex computationally expensive algorithms are used to predict radiation doses, to organs in the human body from exposure to internally deposited radionuclides. These algorithms contain many inputs, the true values of which are uncertain. Current methods for assessing the effects of the input uncertainties on the output of the algorithms are based on Monte Carlo analyses, i.e. sampling from subjective prior distributions that represent the uncertainty on each input, evaluating the output of the model and calculating sample statistics. For complex computationally expensive algorithms, it is often not possible to get a large enough sample for a meaningful uncertainty analysis. This thesis presents an alternative general theory for uncertainty analysis, based on the use of stochastic process models, in a Bayesian context. The measures provided by the Monte Carlo analysis are obtained, plus extra more informative measures, but using a far smaller sample. The theory is initially developed in a general form and then specifically for algorithms with inputs whose uncertainty can be characterised by independent normal distributions. The Monte Carlo and Bayesian methodologies are then compared using two practical examples. The first example, is based on a simple model developed to calculate doses due to radioactive iodine. This model has two normally distributed uncertain parameters and due to its simplicity an independent measurement of the true uncertainty on the output is available for comparison. This exercise appears to show that the Bayesian methodology is superior in this simple case. The purpose of the second example is to determine if the methodology is practical in a 'real-life' situation and to compare it with a Monte Carlo analysis. A model for calculating doses due to plutonium contamination is used. This model is computationally expensive and has fourteen uncertain inputs. The Bayesian analysis compared favourably to the Monte Carlo, indicating that it has the potential to provide more accurate uncertainty analyses for the parameters of computationally expensive algorithms.

Monte Carlo method has been successfully applied in simulating the particles transport problems. Most of the Monte Carlo simulation tools are static and they can only be used to perform the static simulations for the problems with fixed physics and geometry settings. Proton therapy is a dynamic treatment technique in the clinical application. In this research, we developed a method to perform the dynamic Monte Carlo simulation of proton therapy using Geant4 simulation toolkit. A passive-scattering treatment nozzle equipped with a rotating range modulation wheel was modeled in this research. One important application of the Monte Carlo simulation is to predict the spatial dose distribution in the target geometry. For simplification, a mathematical model of a human body is usually used as the target, but only the average dose over the whole organ or tissue can be obtained rather than the accurate spatial dose distribution. In this research, we developed a method using MATLAB to convert the medical images of a patient from CT scanning into the patient voxel geometry. Hence, if the patient voxel geometry is used as the target in the Monte Carlo simulation, the accurate spatial dose distribution in the target can be obtained. A data analysis tool?root was used to score the simulation results during a Geant4 simulation and to analyze the data and plot results after simulation. Finally, we successfully obtained the accurate spatial dose distribution in part of a human body after treating a patient with prostate cancer using proton therapy.

Thesis (Ph.D. - Hydrology and Water Resources)--University of Arizona. / Includes bibliographical references (leaves 332-356).