Experimental Study and Empirical Modeling of Long Term Annealing of the ATLAS18 Strip Sensors
Bibliographic record
Abstract
In order to continue the program of the LHC, the accelerator will be upgraded to the High Luminosity LHC (HL-LHC), which will have a design luminosity of $5 \times 10^{34} cm^{-2}s^{-1}$ , an order of magnitude greater than the present machine. In order to meet the occupancy and radiation hardness requirements resulting from this increase in luminosity, the present ATLAS tracking detector must be replaced. The ATLAS Collaboration is constructing a new central tracking system based completely on silicon sensors. In order to satisfy the radiation hardness requirements we have developed a new n-in-p sensor design. Extensive studies have shown that it results in detectors which comfortably reach the required end-of-life performance. The latest sensor layouts prepared for preproduction, known as ATLAS18, implement this design. However, as well as knowing the performance after a given irradiation fluence, operational considerations require an understanding of the time development of the annealing and resulting variation of the collected charge, of irradiated detectors at different temperatures. Here we describe the measurement of charge collection performance as a function of irradiated fluence and long term annealing time. We also describe a semi-empirical model based on these measurements which allows us to predict the end-of-life charge collection as a function of the temperature profile during operation of the detector. The use of the model to study the effect of annealing on the strip detector at a radius of 40 cm and an integrated irradiation fluence of $\textrm{1.6} \times \textrm{10}^{15} \ \textrm{24}~\textrm{MeV}~\textrm{neutron}~\textrm{ equiv}~\textrm{cm}^{-2}$ is presented.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".