B-flavor tagging at Belle II
Bibliographic record
Abstract
Abstract We report on new flavor tagging algorithms developed to determine the quark-flavor content of bottom ( "Image missing") mesons at Belle II. The algorithms provide essential inputs for measurements of quark-flavor mixing and charge-parity violation. We validate and evaluate the performance of the algorithms using hadronic "Image missing" decays with flavor-specific final states reconstructed in a data set corresponding to an integrated luminosity of 62.8 fb $$^{-1}$$ - 1 , collected at the "Equation missing" resonance with the Belle II detector at the SuperKEKB collider. We measure the total effective tagging efficiency to be $$\begin{aligned} \varepsilon _\mathrm{eff} = \big (30.0 \pm 1.2(\text {stat}) \pm 0.4(\text {syst})\big )\% \end{aligned}$$ ε eff = ( 30.0 ± 1.2 ( stat ) ± 0.4 ( syst ) ) % for a category-based algorithm and $$\begin{aligned} \varepsilon _\mathrm{eff} = \big (28.8 \pm 1.2(\text {stat}) \pm 0.4(\text {syst})\big )\% \end{aligned}$$ ε eff = ( 28.8 ± 1.2 ( stat ) ± 0.4 ( syst ) ) % for a deep-learning-based algorithm.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".