Implementation and performance of the seeded reconstruction for the ATLAS event filter
Bibliographic record
Abstract
ATLAS is one of the four major Large Hadron Collider (LHC) experiments that will start data taking in 2007. It is designed to cover a wide range of physics topics. The ATLAS trigger system has to be able to reduce an initial 40 MHz event rate, corresponding to an average of 23 proton-proton inelastic interactions per every 25 ns bunch crossing, to 200 Hz admissible by the Data Acquisition System. The ATLAS trigger is divided in three different levels. The first one provides a signal describing an event signature using dedicated custom hardware. This signature must be confirmed by the High Level Trigger (HLT) which using commercial computing farms performs an event reconstruction by running a sequence of algorithms. The validity of a signature is checked after every algorithm execution. A main characteristic of the ATLAS HLT is that only the data in a certain window around the position flagged by the first level trigger are analyzed. In this work, the performance of one sequence that runs at the Event Filter level (third level) is demonstrated. The goal of this sequence is to reconstruct and identify high transverse momentum electrons by performing cluster reconstruction at the electromagnetic calorimeter, track reconstruction at the Inner Detector, and cluster track matching.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".