Performance and efficiency of a transformer-based quark/gluon jet tagger in the ATLAS experiment
Bibliographic record
Abstract
A deep-learning approach based on the transformer architecture is developed to distinguish between jets originating from quarks and gluons. The algorithm operates on jets with transverse momentum $p_{\text{T}} > 20$ and pseudorapidity $|η| < 4.5$ and takes as input several properties derived from the jet constituents, using information from the ATLAS detector's tracker and calorimeter. The algorithm's performance is evaluated by analyzing dijet data events from proton-proton collisions at $\sqrt{s} = 13$ and $13.6$ TeV during Run 2 and Run 3 of the Large Hadron Collider. Two methods are used to obtain distributions from quark- or gluon-initiated jets in data: a matrix method fully based on Monte Carlo simulation and a new approach named `jet topics' which has less dependence on the modelling of the physics process under study. The quark and gluon identification efficiencies measured in data for the 50% quark-identification-efficiency working point vary from the simulated ones for quark-initiated (gluon-initiated) jets by factors of 0.88-1.30 (0.61-1.05) with uncertainties of 10%-70% (10%-95%). The uncertainties estimated with the jet topics method are smaller than those estimated with the matrix method, with up to 20% less systematic uncertainty in some phase-space regions. The advances in jet identification reported here provide a robust tool for precision Standard Model measurements and searches for new physics at the LHC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".