Test and extraction methods for the QC parameters of silicon strip sensors for ATLAS upgrade tracker
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Quality Control (QC) of pre-production strip sensors for the Inner Tracker (ITk) of the ATLAS Inner Detector upgrade has finished, and the collaboration has embarked on the QC test programme for production sensors. This programme will last more than 3 years and comprises the evaluation of approximately 22000 sensors. 8 Types of sensors, 2 barrel and 6 endcap, will be measured at many different collaborating institutes. The sustained throughput requirement of the combined QC processes is around 500 sensors per month in total. Measurement protocols have been established and acceptance criteria have been defined in accordance with the terms agreed with the supplier. For effective monitoring of test results, common data file formats have been agreed upon across the collaboration. To enable evaluation of test results produced by many different test setups at the various collaboration institutes, common algorithms have been developed to collate, evaluate, plot and upload measurement data. This allows for objective application of pass/fail criteria and compilation of corresponding yield data. These scripts have been used to process the data of more than 3000 sensors so far, and have been instrumental for identification of faulty sensors and monitoring of QC testing progress.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it