Magnetostatic Modelling and Measurement for Particle Dynamics of a Magnetic Energy Filter
Bibliographic record
Abstract
This thesis discusses the modelling and measurement of electromagnetic fields in a filter designed to selectively filter low-energy electrons from beams produced by electron sources. Two electron source configurations—based on a direct current (DC) 0.5 MeV electron gun and a 3.5 MeV radio frequency (RF) electron gun available at the Canadian Light Source (CLS) test laboratory—were examined. Magnetostatic simulations were conducted using two-dimensional modelling software, and the results were validated against experimental measurements using a magnetic energy filter system at CLS. This process enabled the calibration of magnetic field settings for optimized performance in the test laboratory. The primary settings of interest were calculated, which are the operating currents needed for each of the magnets within. Particle tracking software was subsequently employed to simulate a beam of electrons traversing the energy filter, allowing for the generation of trajectory plots and evaluation of the momentum spread before and after filtration. The calculated operating parameters for the magnetic filter were as follows: for the DC electron gun, the dipole magnets required a current of 0.697 A, while the entrance and exit focusing quadrupoles operated at 0.195 A, the center focusing quadrupole at 0.229 A, and the defocusing quadrupoles at −0.256 A. For the RF electron gun, the dipole current was found to be 4.88 A, with the corresponding quadrupole currents at 1.37 A, 1.61 A, and 1.80 A. Both configurations successfully achieved a reduction in the momentum spread by eliminating low-energy electrons; however, the RF setup allowed substantially higher beam current densities, up to 9.14 A/mm2. Overall, the findings demonstrated that the modelled filter effectively refined the electron beam qualities for both electron guns. The methodology was shown to be adaptable to other sources with beam energies below 11.9 MeV, within the linear operating range of the model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".