Performance Studies for Electron and Photon Selection at the Event Filter
Bibliographic record
Abstract
In this note the electron and photon selection potential of the event filter is studied. The offline software suite ATRECON is used to investigate the rejection power achievable within the stringent constraints in an online environment. We used the electro-magnetic calorimeter reconstruction, the xKalman and iPatRec pattern recognition packages, and for photon conversion finding xConver/xHouRec. The interplay between efficiency/rejection and the execution time of the algorithms is investigated for electrons and photons both at low and high luminosity. A total efficiency of about 75(73)% for single electrons with Pt=20(30)GeV at a dijet rate of ~40(130)Hz at low (high) luminosity can be retained while reducing the median reconstruction time by a factor of ~3(10) with simple reconfigurations of ATRECON.Additional, the long tails seen in the reconstruction time distribution at the default settings are reduced significantly.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".