Integration and Commissioning of the Software-based Readout System for ATLAS Level-1 Endcap Muon Trigger in Run 3
Bibliographic record
Abstract
The Large Hadron Collider and the ATLAS experiment at CERN will explore new frontiers in physics in Run 3 starting in 2022. In the Run 3 ATLAS Level-1 endcap muon trigger, new detectors called New Small Wheel and additional Resistive Plate Chambers will be installed to improve momentum resolution and to enhance the rejection of fake muons. The Level-1 endcap muon trigger algorithm will be processed by new trigger processor boards with modern FPGAs and high-speed optical serial links. For validation and performance evaluation, the inputs and outputs of their trigger logic will be read out using a newly developed software-based readout system. We have successfully integrated this readout system in the ATLAS online software framework, enabling commissioning in the actual Run 3 environment. Stable trigger readout has been realized for input rates up to 100 kHz with a developed event-building application. We have verified that its performance is sufficient for Run 3 operation in terms of event data size and trigger rate. The paper will present the details of the integration and commissioning of the software-based readout system for ATLAS Level-1 endcap muon trigger in Run 3.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".