Seyyed Farhad Masoudi

Recent advancements in light source accelerators have significantly enhanced beam quality. These accelerators are continually upgraded to meet scientific demands. A key element of their design is the precise control of particle orbits and the correction of errors to maintain a stable, high-quality beam. To facilitate the automatic electron beam position correction, we propose a computational framework to control the beam based on the Elettra 2.0 storage ring lattice, a process commonly known as orbit control. Our method exclusively relies on beam position monitors to predict the correctors’ magnetic field strength using a deep convolutional neural network. We aimed to train our system and controller deep learning models sequentially, adopting a transfer learning strategy to fine-tune the controller and bring the final orbit closer to reference orbit. Since Elettra 2.0 is still in the design and construction stage, the proposed method is evaluated entirely in simulation. Results demonstrated the effectiveness and efficiency of our approach in orbit control, showcasing robust generalizability to real-world applications. Notably, when applied to the Elettra 2.0 storage ring, our method achieved 7 additional percentage-points of suppression stability relative to the uncorrected orbit over the singular value decomposition method. The proposed computational framework can also be applied to other synchrotrons, thereby facilitating the use of machine learning in accelerators.

PurposeSpecial properties and recent advances in the synthesis and biomolecular functionalization of gold nanoparticles (GNPs) have led to the evolution of their use in biomedical applications such as photon radiotherapy. Simulation-based studies on the effect of various parameters that govern the dose enhancement due to utilizing GNPs have facilitated the progress of knowledge in this field. Due to their flexibility and easier accessibility compared with experimental works, simulations have the potential to be considered for pre-clinical tests and, therefore, should be close to the realistic conditions as much as possible.Materials and methodsTo this aim, the present work investigates the effect of the presence of GNPs that are accumulated in the cytoplasm of the constituent cells in healthy tissues of a human eye phantom, inspired by the published experimental results which report that non-target tissues also receive the drugs containing GNPs. The GNPs' concentrations are assumed to decrease by moving from the tumor toward the depth of the phantom through a suggested pattern. The MCNPX Monte Carlo code is used for the simulations.ResultsThe results show that for four concentrations tested, the dose enhancement factor in the shallower layer reaches 6, and decreases to 1.2 in the last layer. The dose enhancements are also examined for critical structures of the iris, cornea, sclera, and lens, showing maximum deviations of about 3 to 200% compared with the absence of GNPs in the healthy tissue. Considering the reported doses to the lens by clinical institutions, the effect of penetration of GNPs to deep layers on treatment time is also investigated.ConclusionsThe results show that the penetration of GNPs from the tumor toward healthy tissues strongly controls the dose enhancement over the various eye structures and emphasizes the importance of modeling the GNPs' distribution in the medium on the overall dose enhancement. Considering the current challenges in the clinical use of GNPs, more effort needs to be made to reach an effective endpoint in treatment.

As a powerful, non-destructive analysis tool based on thermal neutron capture reaction, prompt gamma neutron activation analysis (PGNAA) indeed requires the appropriate neutron source. Neutrons produced by electron Linac-based neutron sources should be thermalized to be appropriate for PGNAA. As a result, thermalization devices (TDs) are used for the usual fast neutron beam to simultaneously maximize the thermal neutron flux and minimize the non- thermal neutron flux at the beam port of TD. To achieve the desired thermal neutron flux, the optimized geometry of TD including the proper materials for moderators and collimator, as well as the optimized dimensions are required. In this context, TD optimization using only Monte Carlo approaches such as MCNP is a multi-parameter problem and time-consuming task. In this work, multilayer perceptron (MLP) neural network has been applied in combination with Q-learning algorithm to optimize the geometry of TD containing collimator and two moderators. Using MLP, both thickness and diameter of the collimator at the beam port of TD have first been optimized for different input electron energies of Linac as well as for moderators’ thickness values and the collimator. Then, the MLP has been learned by the thermal and non-thermal neutron flux simultaneously at the beam port of TD calculated by MCNPX2.6 code. After selecting the optimized geometry of the collimator, a combination of Q-learning algorithm and MLP artificial neural network have been used to find the optimal moderators’ thickness for different input electron energies of Linac. Results verify that the final optimum setup can be obtained based on the prepared dataset in a considerably smaller number of simulations compared to conventional calculation methods as implemented in MCNP.

The increasing use of nuclear technology in various fields makes it necessary to provide the required safety to work with this industry. Gamma source is one of the most widely used sources in industry and medicine. Finding a lost gamma source in a gamma irradiation room without human presence is challenging due to the particular arrangements and barriers in the room for radiation shielding and requires an efficient and robust method. In this paper, locating and routing the lost gamma source in the gamma irradiation room containing radiation blocking barriers are done simultaneously by using two methods, convolutional neural network (CNN) and Q-learning, which are powerful algorithms for deep learning and machine learning. Environment simulation with gamma source was performed using Geant4 simulation. The results show that by combining these two methods in geometries with radiation blocking barriers, in addition to locating with 90% accuracy, routing can also be performed. Although the presence of thick barriers in the room reduces the accuracy, increases the time required to finding the lost gamma source or the inefficiency of other methods, nevertheless, the results show that combination of CNN and Q-learning reduces the time and greatly increases the accuracy.