SECONDARY ELECTRON EMISSION FROM THIN ALUMINIUM FOILS PRODUCED BY HIGH ENERGY ELECTRON BEAMS

The description of the experimental equipment and technique for measuring the secondary emission of elec-trons (SEE) with application of accelerated electrons at the linear accelerator of the IHEPNP NSC KIPT with ener-gies up to 30 MeV and a standard secondary emission monitor [1] are presented. Experimental data of secondary electron emission yields from thin aluminum targets (8 and 50 μm) for primary electron beam energies of 16 and 25 MeV have been experimentally measured. The analysis of the experimental data and their comparison with the theory are carried out. It is shown that the proposed technique for measuring the yields of secondary electron emis-sion is useful and applied for study of low-energy and δ-electrons yields from thin foils, as well as to research the effect of the density effect depending on the energy of the primary electron beam.

DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo software

Background Any Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any single accelerator are unique and generally unknown, an appropriate model of an electron beam must be assumed before MC simulations can be run. The purpose of the present study is to develop a flexible framework with suitable regression models for estimating parameters of the model of primary electron beam in simulators of medical linear accelerators using real reference dose profiles measured in a water phantom. Methods All simulations were run using PRIMO MC simulator. Two regression models for estimating the parameters of the simulated primary electron beam, both based on machine learning, were developed. The first model applies Principal Component Analysis to measured dose profiles in order to extract principal features of the shapes of the these profiles. The PCA-obtained features are then used by Support Vector Regressors to estimate the parameters of the model of the electron beam. The second model, based on deep learning, consists of a set of encoders processing measured dose profiles, followed by a sequence of fully connected layers acting together, which solve the regression problem of estimating values of the electron beam parameters directly from the measured dose profiles. Results of the regression are then used to reconstruct the dose profiles based on the PCA model. Agreement between the measured and reconstructed profiles can be further improved by an optimization procedure resulting in the final estimates of the parameters of the model of the primary electron beam. These final estimates are then used to determine dose profiles in MC simulations. Results Analysed were a set of actually measured (real) dose profiles of 6 MV beams from a real Varian 2300 C/D accelerator, a set of simulated training profiles, and a separate set of simulated testing profiles, both generated for a range of parameters of the primary electron beam of the Varian 2300 C/D PRIMO simulator. Application of the two-stage procedure based on regression followed by reconstruction-based minimization of the difference between measured (real) and reconstructed profiles resulted in achieving consistent estimates of electron beam parameters and in a very good agreement between the measured and simulated photon beam profiles. Conclusions The proposed framework is a readily applicable and customizable tool which may be applied in tuning virtual primary electron beams of Monte Carlo simulators of linear accelerators. The codes, training and test data, together with readout procedures, are freely available at the site: https://github.com/taborzbislaw/DeepBeam.

DeepBeam – A Machine Learning Framework For Tuning The Primary Electron Beam of The PRIMO Monte Carlo Software

BackgroundAny Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any single accelerator are unique and generally unknown, an appropriate model of an electron beam must be assumed before MC simulations can be run. The purpose of the present study is to develop a flexible framework with suitable regression models for estimating parameters of the model of primary electron beam in simulators of medical linear accelerators, basing on real reference dose profiles measured in a water phantom. MethodsAll simulations were run using PRIMO MC simulator. Two regression models for estimating the parameters of the simulated primary electron beam, both based on machine learning, were developed. The first model applies Principal Component Analysis to measured dose profiles in order to extract principal features of the shapes of the these profiles. The PCA-obtained features are then used by Support Vector Regressors to estimate the parameters of the model of the electron beam. The second model, based on deep learning, consists of a set of encoders processing measured dose profiles, followed by a sequence of fully connected layers acting together, which solve the regression problem of estimating values of the electron beam parameters directly from the measured dose profiles. Results of the regression are then used to reconstruct the dose profiles, basing on the PCA model. Agreement between the measured and reconstructed profiles can be further improved by an optimization procedure resulting in the final estimates of the parameters of the model of the primary electron beam. These final estimates are then used to determine dose profiles in MC simulations.ResultsAnalysed were a set of actually measured (real) dose profiles of 6 MV beams from a real Varian 2300 C/D accelerator, a set of simulated training profiles, and a separate set of simulated testing profiles, both generated for a range of parameters of the primary electron beam of the Varian 2300 C/D PRIMO simulator. Application of the two-stage procedure based on regression followed by reconstruction-based minimization of the difference between measured (real) and reconstructed profiles resulted in achieving consistent estimates of electron beam parameters and in a very good agreement between the measured and simulated photon beam profiles.ConclusionsThe proposed framework is a readily applicable and customizable tool which may be applied in tuning virtual primary electron beams of Monte Carlo simulators of linear accelerators. The codes, training and test data, together with some trained models and readout procedures, are freely available at the site: https://github.com/taborzbislaw/DeepBeam.

Theoretical Simulation of Backscattering Electron Coefficient for SixGe1-x/Si Heterostructure as a Function of Primary Electron Beam Energy and Ge Concentration

This study aims to investigate the backscattering electron coefficient for SixGe1-x/Si heterostructure sample as a function of primary electron beam energy (0.25-20 keV) and Ge concentration in the alloy. The results obtained have several characteristics that are as follows: the first one is that the intensity of the backscattered signal above the alloy is mainly related to the average atomic number of the SixGe1-x alloy. The second feature is that the backscattering electron coefficient line scan shows a constant value above each layer at low primary electron energies below 5 keV. However, at 5 keV and above, a peak and a dip appeared on the line scan above Si-Ge alloy and Si, respectively, close to the interfacing line. Furthermore, the shape and height of peak and dip broadening depend on the primary electron energy and incidence position with respect to the interfacing line. The last feature is that the spatial resolution of the backscattered signal at the interfacing line is improving by decreasing the primary electron energy (below 5 keV) and the shared element (Si) concentration. On the other hand, a poor compositional contrast has been shown at low primary electron energy below 5 keV. For energies above 5 keV, the spatial resolution becomes weak. These results can be explained by the behavior of the incident electrons inside the solid (interaction volume), especially at a distance close to the interfacing line and their chance to backscatter out of the sample. In general, a good compositional contrast with a high spatial resolution can be achieved at primary electron energy equal to 1 keV. Keywords: Monte Carlo model, Backscattering electron coefficient, Si-Ge/Si, Elastic scattering, Spatial resolution, Compositional contrast.

OPERATION OF AN INDUSTRIAL ELECTRON ACCELERATOR IN A DOUBLE-BEAM e,X-MODE

Analysis of mixed e,X-radiation formation in output devices of an industrial electron accelerator is conducted. The possibility is demonstrated to obtain an extra radiation channel on the basis of a practically free source of Xrays simultaneously with the main channel of product processing with electron beam. The conditions of production of the secondary radiation in the state of electronic equilibrium at product treatment with scanning electron beam in the main radiation channel are studied by means of computer simulation. The dependence of spatial radiant characteristics of the X-ray radiation on the spectrum of a primary electron beam and surface density of a treated load has been established. For an industrial accelerator LU-10 of NSC KIPT, the regime of object processing in the extra radiation channel is examined. The results of calculation of the X-ray dose rate and its spatial distribution are in good agreement with the experimental data. The comparative capacity of both radiation channels of the plant is analyzed. The extra radiation source can be used for the execution of non-commercial programs like sanitation of cultural artefacts.

Nanoscale Automated Quantitative Mineralogy: A 200-nm Quantitative Mineralogy Assessment of Fault Gouge Using Mineralogic

Effective energy-dispersive X-ray spectroscopy analysis (EDX) with a scanning electron microscope of fine-grained materials (submicrometer scale) is hampered by the interaction volume of the primary electron beam, whose diameter usually is larger than the size of the grains to be analyzed. Therefore, mixed signals of the chemistry of individual grains are expected, and EDX is commonly not applied to such fine-grained material. However, by applying a low primary beam acceleration voltage, combined with a large aperture, and a dedicated mineral classification in the mineral library employed by the Zeiss Mineralogic software platform, mixed signals could be deconvoluted down to a size of 200 nm. In this way, EDX and automated quantitative mineralogy can be applied to investigations of submicrometer-sized grains. It is shown here that reliable quantitative mineralogy and grain size distribution assessment can be made based on an example of fault gouge with a heterogenous mineralogy collected from Ikkattup nunaa Island, southern West Greenland.

Monte Carlo modelling of clinical accelerator beams and estimation of primary electron beam parameters

The accuracy of Monte Carlo simulations of clinical photon beams in radiation oncology is dependent on the linac head model accuracy and on parameters of the primary electron beam. While the internal composition and geometry of the accelerator head are known precisely, at least in principle, the energy spectrum and the spatial characteristics of the primary electron beam are unknown and immeasurable. The mean energy and FWHM of the electron beam are commonly estimated by comparing the simulation results with measured dosimetric data. Percentage depth doses (PDDs) and dose profiles are sensitive to changes in the electron beam parameters and are therefore in general used for the comparison. In the published studies which deal with parameter estimation, the determination of electron beam parameters is typically performed through a trial and error process. As to the parameter optimization, there is no unified methodology agreed upon, and the uncertainty of the resulting parameter values is usually not quantified by the authors. The aim of our work was not only to estimate the mean energy and the FWHM of the primary electron beam, but also to determine the confidence region of the optimized values in a defined and repeatable way. A model of Varian Clinac 2100C/D linear accelerator 6 MV photon beam was built in the EGSnrc/BEAMnrc Monte Carlo system. PDDs and dose profiles for different field sizes and different depths were obtained from water phantom measurements. We show that an approach based on a large number of simulations, each with a relatively low number of primary particles, in combination with non-linear regression methods allows to find both the optimized values of the electron beam parameters and their common 95% confidence region.

The role of low-energy electrons in focused electron beam induced deposition: four case studies of representative precursors

Focused electron beam induced deposition (FEBID) is a single-step, direct-write nanofabrication technique capable of writing three-dimensional metal-containing nanoscale structures on surfaces using electron-induced reactions of organometallic precursors. Currently FEBID is, however, limited in resolution due to deposition outside the area of the primary electron beam and in metal purity due to incomplete precursor decomposition. Both limitations are likely in part caused by reactions of precursor molecules with low-energy (<100 eV) secondary electrons generated by interactions of the primary beam with the substrate. These low-energy electrons are abundant both inside and outside the area of the primary electron beam and are associated with reactions causing incomplete ligand dissociation from FEBID precursors. As it is not possible to directly study the effects of secondary electrons in situ in FEBID, other means must be used to elucidate their role. In this context, gas phase studies can obtain well-resolved information on low-energy electron-induced reactions with FEBID precursors by studying isolated molecules interacting with single electrons of well-defined energy. In contrast, ultra-high vacuum surface studies on adsorbed precursor molecules can provide information on surface speciation and identify species desorbing from a substrate during electron irradiation under conditions more representative of FEBID. Comparing gas phase and surface science studies allows for insight into the primary deposition mechanisms for individual precursors; ideally, this information can be used to design future FEBID precursors and optimize deposition conditions. In this review, we give a summary of different low-energy electron-induced fragmentation processes that can be initiated by the secondary electrons generated in FEBID, specifically, dissociative electron attachment, dissociative ionization, neutral dissociation, and dipolar dissociation, emphasizing the different nature and energy dependence of each process. We then explore the value of studying these processes through comparative gas phase and surface studies for four commonly-used FEBID precursors: MeCpPtMe3, Pt(PF3)4, Co(CO)3NO, and W(CO)6. Through these case studies, it is evident that this combination of studies can provide valuable insight into potential mechanisms governing deposit formation in FEBID. Although further experiments and new approaches are needed, these studies are an important stepping-stone toward better understanding the fundamental physics behind the deposition process and establishing design criteria for optimized FEBID precursors.