Setups for eliminating static charge of the ATLAS18 strip sensors
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Bibliographic record
Abstract
Abstract Construction of the new all-silicon Inner Tracker (ITk), developed by the ATLAS collaboration to be able to track charged particles produced at the High-Luminosity LHC, started in 2020 and is expected to continue till 2028. The ITk detector will include 18,000 highly segmented and radiation hard n+-in-p silicon strip sensors (ATLAS18), which are being manufactured by Hamamatsu Photonics. Mechanical and electrical characteristics of produced sensors are measured upon their delivery at several institutes participating in a complex Quality Control (QC) program. The QC tests performed on each individual sensor check the overall integrity and quality of the sensor. During the QC testing of ATLAS18 strip sensors, an increased number of sensors that failed the electrical tests was observed. In particular, IV measurements indicated an early breakdown, while large areas containing several tens or hundreds of neighbouring strips with low interstrip isolation were identified by the Full strip tests, and leakage current instabilities were measured in a long-term leakage current stability setup. Moreover, a high surface electrostatic charge reaching a level of several hundreds of volts per inch was measured on a large number of sensors and on the plastic sheets, which mechanically protect these sensors in their paper envelopes. Accumulated data indicates a clear correlation between observed electrical failures and the sensor charge-up. To mitigate the above-described issues, the QC testing sites significantly modified the sensor handling procedures and introduced sensor recovery techniques based on irradiation of the sensor surface with UV light or application of intensive flows of ionized gas. In this presentation, we will describe the setups implemented by the QC testing sites to treat silicon strip sensors affected by static charge and evaluate the effectiveness of these setups in terms of improvement of the sensor performance.
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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 it