Thesis Defense |
Events During the Week of July 20th through July 27th, 2025
Monday, July 21st, 2025
- No events scheduled
Tuesday, July 22nd, 2025
- Design and Operation of a Novel Spectrometer Diagnostic at DIII-D for the Measurement of Magnetic Pitch and Toroidal Rotation at ELM-Relevant Timescales
- Time: 1:00 pm - 3:00 pm
- Place: ERB 106 or
- Speaker: Ryan Albosta, Physics PhD Graduate Student
- Abstract: Two novel single-channel Czerny-Turner spectrometers [1&2] have been deployed at the DIII-D tokamak for simultaneous and co-located measurements of the full spectrally-resolved Motional Stark Effect (MSE) Split Deuterium Beam Emission and the Carbon CVI charge exchange emission at high spectral (∆λ=0.13 nm) and temporal resolution (1-5 kHz). High throughput optics (f/#=2.8) allow for good signal-to-noise at high time resolution using fast EMCCD detectors. Initial results from a 2024 DIII-D ITER-similar H-mode experiment at DIII-D show clear toroidal rotation collapse during ELM crashes from the CER channels, accompanied by Sigma/Pi ratio changes over a similar timescale from the MSE channels. However, non-equilibrium neutral beam excited state populations appear to play a large role in pedestal MSE line ratios, as qualitatively predicted by the NOMAD quantum collisional codebase. Complex neutral beam physics has also been observed and successfully reproduced with a physics forward model, revealing a continuum of beam energies that further complicate the MSE spectrum. These effects and their impact on the MSE fitting process during fast ELM dynamics in the DIII-D pedestal will be discussed, as well as the implications for fast magnetic beam-emission measurements – and therefore bootstrap current analysis – in H-mode pedestals generally. Simultaneous magnetic pitch, beam populations, and rotation measurements will be shown during type-1 ELMs at 5 kHz, along with next steps towards further disentangling population and pitch effects within the MSE spectrum during large density fluctuations. [1] Albosta R et al.: Rev Sci Instrum. 2022
[2] Albosta R et al.: Rev Sci Instrum. 2024
*Supported by US DOE grants DE-FG02-89ER53296, DE-FG02-08ER54999 and DE-FC02-04ER54698. - Host: Benedikt Geiger and John Sarff
Wednesday, July 23rd, 2025
- No events scheduled
Thursday, July 24th, 2025
- No events scheduled
Friday, July 25th, 2025
- Search for Dark Matter with the ATLAS Detector and Development of a Novel Track Reconstruction Algorithm based on Graph Neural Networks for the ATLAS Inner Tracker
- Time: 10:00 am - 12:00 pm
- Place:
- Speaker: Tuan Minh Pham, Physics PhD Graduate Student
- Abstract: This thesis is divided into two main parts. The first part presents a summary of dark matter searches performed by the ATLAS experiment and a statistical combination of the three most sensitive analyses. The results are interpreted within the framework of a Two-Higgs-Doublet Model extended by a pseudoscalar mediator (2HDM+a). These analyses are based on 139 fb-1 of proton-proton collision data collected at a center-of-mass energy of 13 TeV during Run 2 of the LHC. The combined analyses target final states involving large missing transverse energy and a visible signature from the decay of a Standard Model Higgs boson or Z boson, as well as processes involving the production of charged Higgs bosons. This work provides the most comprehensive set of constraints on the 2HDM+a model published by ATLAS to date.
The second part focuses on the reconstruction of charged-particle tracks in the ATLAS Inner Tracker (ITk), which is confronted with the extreme pile-up conditions expected in the High-Luminosity phase of the Large Hadron Collider (HL-LHC). Given the anticipated increase in instantaneous luminosity and associated event complexity—resulting in up to 200 simultaneous interactions per bunch crossing—traditional reconstruction algorithms face significant computational challenges. To address this, a novel track reconstruction algorithm based on Graph Neural Networks (GNNs) has been developed and evaluated. Using full detector simulation data on realistic ITk geometry, we demonstrate competitive physics performance of the GNN-based tracking approach with respect to the current tracking algorithm. The computational efficiency is optimized and measured in detail. This approach shows significant potential for efficient pattern recognition in dense detector environments, leveraging modern hardware accelerators such as GPUs and FPGAs for fast and scalable event reconstruction. - Host: Sau Lan Wu