Description and stability of a RPC-based calorimeter in electromagnetic and hadronic shower environments
Bibliographic record
Abstract
Abstract The CALICE Semi-Digital Hadron Calorimeter technological prototype completed in 2011 is a sampling calorimeter using Glass Resistive Plate Chamber (GRPC) detectors as the active medium. This technology is one of the two options proposed for the hadron calorimeter of the International Large Detector for the International Linear Collider. The prototype was exposed in 2015 to beams of muons, electrons, and pions of different energies at the CERN Super Proton Synchrotron. The use of this technology for future experiments requires a reliable simulation of its response that can predict its performance. GEANT4 combined with a digitization algorithm was used to simulate the prototype. It describes the full path of the signal: showering, gas avalanches, charge induction, and hit triggering. The simulation was tuned using muon tracks and electromagnetic showers for accounting for detector inhomogeneity and tested on hadronic showers collected in the test beam. This publication describes developments of the digitization algorithm. It is used to predict the stability of the detector performance against various changes in the data-taking conditions, including temperature, pressure, magnetic field, GRPC width variations, and gas mixture variations. These predictions are confronted with test beam data and provide an attempt to explain the detector properties. The data-taking conditions such as temperature and potential detector inhomogeneities affect energy density measurements but have small impact on detector efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".