Sensor response and radiation damage effects for 3D pixels in the ATLAS IBL Detector
Bibliographic record
Abstract
Pixel sensors in 3D technology equip the outer ends of thestaves of the Insertable B Layer (IBL), the innermost layer of theATLAS Pixel Detector, which was installed before the start of LHCRun 2 in 2015. 3D pixel sensors are expected to exhibit moretolerance to radiation damage and are the technology of choice forthe innermost layer in the ATLAS tracker upgrade for the HL-LHCprogramme. While the LHC has delivered an integrated luminosity of ≃ 235 fb$^{-1}$ since the start of Run 2, the 3D sensorshave received a non-ionising energy deposition corresponding to afluence of ≃ 8.5 × 10$^{14}$ 1 MeVneutron-equivalent cm$^{-2}$ averaged over the sensor area. Thispaper presents results of measurements of the 3D pixel sensors'response during Run 2 and the first two years of Run 3, withpredictions of its evolution until the end of Run 3 in 2025. Dataare compared with radiation damage simulations, based on detailedmaps of the electric field in the Si substrate, at various fluencelevels and bias voltage values. These results illustrate thepotential of 3D technology for pixel applications in high-radiationenvironments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".