Related papers: Machine learning for analysis of plasma driven Ion…
The production of ion beams from the interaction of a circularly polarized laser pulse with a nanometric double-layer target is discussed in the regime where all electrons are expelled from the target by laser radiation pressure.…
Laser-driven ion acceleration from nanostructured targets offers a promising route to compact, high-energy ion sources. In this work, we demonstrate through particle-in-cell simulations that rectangular nanoring targets significantly…
Machine learning (ML) provides a broad spectrum of tools and architectures that enable the transformation of data from simulations and experiments into useful and explainable science, thereby augmenting domain knowledge. Furthermore,…
When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule. While popular, this…
The essay gives an overview on researches in the field of laser ion acceleration, focusing on two types of targets. There are many types of targets while they can all be divided into targets that apply single ion or multiple ions. Mixed…
Machine learning is applied to investigate the phase transition of two-dimensional complex plasmas. The Langevin dynamics simulation is employed to prepare particle suspensions in various thermodynamic states. Based on the resulted particle…
Ionic liquid ion sources are a promising technology that can be used for many applications from space propulsion to focused ion beam microetching. Ionic liquid ion sources produce ion beams by extracting single ions and metastable solvated…
We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to…
We investigate the usage of a Schlieren imaging setup to measure the geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe light is used to image the plasma channel in a tenuous vapor and machine learning…
We study the application of deep learning techniques to the analysis and classification of ions accelerated at collisionless shocks in hybrid (kinetic ions--fluid electrons) simulations. Ions were classified as thermal, suprathermal, or…
In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle…
Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they…
This thesis is about the designing and constructing a microwave ion source. The ions are generated in a thermal and dense hydrogen plasma by microwave induction. The plasma is generated by using a microwave source with a frequency of 2.45…
Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work,…
A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical…
To this day the interaction of high-intensity lasers with matter is considered to be a possible candidate for next generation particle accelerators. Within the LIGHT collaboration crucial work for the merging of a high-intensity laser…
In nuclear and atomic physics experiments, charged ion beams often need to be guided from the ion production to the experimental site. In the PUMA experiment, an ion source beamline was developed, which can be operated with up to…
Neutral Beam Injectors (NBIs) based on negative ions are the workhorses of future fusion reactors, such as ITER, which they are expected to provide with up to \SI{33}{MW} of power to heat the fusion plasma. The negative hydrogen ions are…
We demonstrate a compact ion beam device capable of accelerating H$^+$ and D$^+$ ions up to 75keV energy, on to a solid target, with sufficient beam current to study fusion reactions. The ion beam system uses a microwave driven plasma…
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…