Online muon reconstruction in the ATLAS level-2 trigger system
Bibliographic record
Abstract
To cope with the 40 MHz event production rate of LHC, the trigger of the ATLAS experiment selects events in three sequential steps of increasing complexity and accuracy whose final results are close to the offline reconstruction. The Level-1, implemented with custom hardware, identifies physics objects within Regions of Interests and operates with a first reduction of the event rate to 75 kHz. The higher trigger levels, Level-2 and Level-3, provide a software based event selection which further reduces the event rate to about 100 Hz. This paper presents the algorithm (/spl mu/Fast) employed at Level-2 to confirm the muon candidates flagged by the Level-1. /spl mu/Fast identifies hits of muon tracks inside the barrel region of the Muon Spectrometer and provides a precise measurement of the muon momentum at the production vertex. The algorithm must process the Level-1 muon output rate (/spl sim/20 kHz), thus particular care has been taken for its optimization. The result is a very fast track reconstruction algorithm with good physics performance which, in some cases, approaches that of the offline reconstruction: it finds muon tracks with an efficiency of about 95% and computes p/sub T/ of prompt muons with a resolution of 5.5% at 6 GeV and 4.0% at 20 GeV. The algorithm requires an overall execution time of /spl sim/1 ms on a 100 SpecInt95 machine and has been tested in the online environment of the Atlas detector test beam.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".