Examensarbeten för masterexamen
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- PostPrioritizing Structures for Neuroevolution Potentials Improving training data selection for machine-learned interatomic potentials(2024) Strandby, Carl; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Erhart; Erhart
- PostNeural Network-based study on background for the Dark Leptonic Scalar model at NA64(2024) Zaya, Emil; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Cederwall, Martin; Crivelli, PaoloThe search for a particle candidate that could explain the origin of dark matter is a central goal in modern astro-particle physics. Numerous experiments employing various measurement strategies are being developed to try and understand this elusive phenomenon. The NA64 experiment situated at the north area of CERN, utilizing the CERN Super Proton Synchrotron (SPS), is an active target experiment aiming to look for signatures like missing energies with hopes of finding signals that correspond to Dark Matter (DM) particles. These dark particles are modelled to explain the physical process of kinetic mixing between the Standard Model (SM) and the hypothesised corresponding Dark Sector (DS). The main purpose of this project is to study the background for a Dark Leptonic Scalar model (DLS) using a highly accurate Monte Carlo simulation for the NA64 experiment. More precisely, the GEANT4 particle simulator was used for the NA64 experiment to simulate the results of the experimental setup used in 2023. The results of this was compared with real data taken in 2023, and a first step was benchmarking the simulation which was done by using dimuon (μμ) events. Furthermore, the simulation results were used as a means of perfecting the methods of event selection. The main source of background for DLS particle φ are μμ production, kaon κ and pion π decay. The main purpose of this thesis is to produce a trained Neural Network (NN) model that can be used for optimizing the selection of events. The background for the DLS φ was simulated and trained on a NN for selecting μμ events as a means of benchmarking the method. The selection of μμ using a trained NN is compared to traditional methods of selection, where an increase of 36 % of the final state events is seen with the NN selected data. A future study could be to simulate the DLS φ particles and train them on a NN to use for event selection. The hopes are to gain a higher signal-to-background ratio and a larger amount of data for the DLS model.
- PostRegeneration of Foam Electrodes Used for for the Removal of Mercury from Aqueous Solutions(2024) Gustafsson, Pontus; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Wickman, Björn; Roth, VeraMercury is a heavy metal of great environmental concern. It possesses great environmental mobility and is highly toxic for both humans and wildlife. An electrochemical mercury decontamination technique that uses Pt-Hg alloy formation to collect mercury from aqueous sources has been developed and shows great promise and many advantages over existing techniques. The goal of this thesis is to study the regenerative capacity of platinum-coated foam electrodes used in this technique over decontamination cycles. Regeneration in this case refers to the re-release of mercury in the form of Hg-ions via the oxidation of Pt-Hg alloy. Using a three-electrode set-up in batch experiments, mercury was removed from 0.5 M sulphuric acid with a mercury concentration of 1000 ppb using a platinum-coated stainless steel foam. Mercury was also removed from contaminated concentrated sulphuric acid from a zinc smelter using a platinum-coated RVC foam. Unfortunately, complete regeneration was not achieved in any experiment, typically releasing less than half of the collected mercury. This partial regeneration is likely due to suboptimal experimental conditions. Despite this, the stability of the foams was demonstrated over multiple formations/regenerations. The problems identified also highlight possible ways forward for future research on the studied mercury decontamination technique. Decontamination with the RVC foam in concentrated sulphuric acid managed to reach mercury levels below the industry standard for high purity, something that has not been presented in previously published research.
- PostSD Map Localization: A Deep Learning Approach(2024) Abdul Rahuman, Sheik Meeran Rasheed; Jathavedan, Sameer; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Granath, Mats; Granath, MatsAccurate localization is critical for the safe and efficient operation of autonomous vehicles, enabling precise navigation and real-time decision-making. This thesis focuses on improving Standard Definition (SD) map based localization, by leveraging deep learning techniques. The research addresses key questions, including how to optimally encode SD map data and sensor data, particularly the Global Navigation Satellite System and Inertial Navigation System sensor, for deep learning, and train models to perform accurate map matching and vehicle localization along the correct road segment. The thesis develops a deep learning-based localization framework for autonomous vehicles, focusing on SD map data. It introduces three main components: a Polyline Encoder using either Graph Neural Networks (GNN) or Transformers, a Map Matching Network based on cross-attention, and a Point Prediction Network consisting of a simple Multi-Layer Perceptron. The model encodes ego trajectories and map links, matches the map data with the vehicle’s trajectory, and predicts precise location. Our results show that the GNN consistently outperforms the Transformer on both map matching and point prediction. The model’s performance varies based on the training and testing data used, with the last point of the trajectory often being sufficient for accurate localization. The study also compares the deep learning model with classical algorithms and finds that the GNN-based localization model significantly improves localization accuracy. Overall, our thesis demonstrates that leveraging deep learning techniques, particularly GNN-based architecture for encoding, along with cross-attention based architecture for map matching, has the potential to significantly enhance SD map localization for autonomous vehicles.
- PostExperimental study of hydrogen trapping by carbides in low alloyed steels using Atom Probe Tomography(2024) Moritz, Ludwig; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Thuvander, Mattias; Jakob, SeverinHydrogen embrittlement (HE) of high-strength low-alloy (HSLA) steels is a big problem. Several options are known to prevent/delay the embrittlement of the steels. One option is to trap the hydrogen at or inside the carbides. This could be interpreted as an energetic interaction, as the nano-sized carbides contribute to the mechanical strength of the material. This work investigates three different low alloyed steels, named in the thesis Steel-1, Ti-steel and V-steel. For the electrochemical D loading process, a 0.1M NaOH in D2O is used. The specimens are transferred at room temperature (RT) to the atom probe (AP). Crystallographic calibration was carried out for all measurements. For Steel-1 in the quenched state, no trapping can be seen. For the annealed specimens, the trapping capability can be argued. The Ti- and V-steel show D trapping. Besides the standard measurement, a room temperature degassing experiment was carried out for the Ti- and V-steel to get a qualitative insight into the strength of the traps.