SiPM Characterization for use in HAICU and Assorted Projects for the ALPHA Experiment
Bibliographic record
Abstract
This report outlines the work completed during the summer of 2024 at both TRIUMF and CERN by Zach Charlesworth, as part of the IPP Student Fellowship Programme, the TRIUMF Summer Student Programme, and the CERN Summer Student Programme. The first half of the summer was spent working with the HAICU collaboration at TRIUMF in Vancouver, Canada, where room temperature characterization of the Hamamatsu VUV4 SiPM was conducted. This characterization included, but was not limited to, determining the signal-to-noise ratio, system dead time, and dark noise rate. The exact procedures for data acquisition, analysis, and the final results are outlined in this report. The second half of the summer was spent working with the ALPHA collaboration at CERN in Geneva, Switzerland. During this time, the ALPHA2 experiment was in operation, so time was divided between shift work for the ALPHA2 experiment, attending the student lecture programme, and working on projects for the ALPHA-g experiment, which was set to start taking data at the end of the summer. The projects completed in preparation for the ALPHA-g experiment are outlined in this report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".