High-level triggers in ATLAS
Bibliographic record
Abstract
The trigger and data-acquisition system of ATLAS, a general-purpose experiment at the Large Hadron Collider (LHC), will be based on three levels of online selection. Starting from the bunch-crossing rate of 40 MHz (an interaction rate of 1 GHz at design luminosity-/spl sim/ 10/sup 34/ cm/sup -2/s/sup -1/), the first level trigger (LVL1) will reduce the rate to about 75 kHz using purpose-built hardware. An additional factor of about 10/sup 3/ in rate reduction is to be provided by the high-level triggers (HLTs) system, with two main functional components: the second-level trigger (LVL2) and the event filter(EF). LVL2 has to provide a fast decision (guided by the information from LVL1), using only a fraction of the full event, however, already at full granularity and can combine all subdetectors. At the EF, a refined selection is made with the. capability of full event reconstruction and the use of detailed calibration and alignment parameters. The HLT software architecture will provide a common and rather "lightweight" framework, able to execute the various selection algorithms and to control the sequence of execution according to the event properties and configuration parameters. System flexibility is a strong requirement in order to adapt to changes, e.g., in luminosity and background conditions. This paper will present the approach chosen for the software design of the HLT selection framework and of the algorithm interface, giving examples for selection sequences and algorithms. Based on currently existing prototypes, results for both the expected physics (signal efficiency, background rejection) and system (execution time) performance will also be shown.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".