Calibration studies of the front-end electronics for the ATLAS New Small Wheel Project
Bibliographic record
Abstract
To continue to probe new avenues of physics, the Large Hadron Collider (LHC) will see a series of upgrades starting in 2019 that will see the luminosity surpass the design specifications. The increased data intake will quickly exceed the capabilities of the current data-acquisition systems of the ATLAS experiment. Therefore, several sub-systems of ATLAS will also undergo a number of upgrades in parallel with the LHC. One of these areas is the New Small Wheel project, which forms one sub-system of the muon detectors. The current detector technology will be replaced with small-strip thin gap chambers (sTGCs) and micro-mesh gaseous detectors (micromegas). This thesis will highlight McGill's role in testing the individual sTGC detectors. Specifically, much of the work presented relates to the preparation of specially designed boards and integrated circuits used in data-acquisition. During this process, both the analog and digital baselines are measured along with any faulty or erratic channels. In doing so, a custom algorithm is developed in order to calculate the pedestal for each readout channel. The results of these scripts are then implemented in a much larger cosmic ray analysis software application that then determines the efficiency of the detector among other metrics of performance.
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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".