An extreme thermal cycling reliability test of ATLAS ITk Strips barrel modules
Bibliographic record
Abstract
Abstract At the end of Run 3 of the Large Hadron Collider (LHC), the accelerator complex will be upgraded to the High-Luminosity LHC (HL-LHC) in order to increase the total amount of data provided to its experiments. To cope with the increased rates of data, radiation, and pileup, the ATLAS detector will undergo a substantial upgrade, including a replacement of the Inner Detector with a future Inner Tracker, called the ITk. The ITk will be composed of pixel and strip sub-detectors, where the strips portion will be composed of 17,888 silicon strip detector modules. During the HL-LHC running period, the ITk will be cooled and warmed a number of times from about -35°C to room temperature as part of the operational cycle, including warm-ups during yearly shutdowns. To ensure ITk Strips modules are functional after these expected temperature changes, and to ensure modules are mechanically robust, each module must undergo ten thermal cycles and pass a set of electrical and mechanical criteria before it is placed on a local support structure. This paper describes the thermal cycling Quality Control (QC) procedure, and results from the barrel pre-production phase (about 5% of the production volume). Additionally, in order to assess the headroom of the nominal QC procedure of 10 cycles and to ensure modules don't begin failing soon after, four representative ITk Strips barrel modules were thermally cycled 100 times — this study is also described.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".