Novel method for the identification of the production flavor of neutral charmed mesons
Bibliographic record
Abstract
We propose a new algorithm for the identification of the production flavor of neutral $D$ mesons in the Belle II experiment. The algorithm exploits the correlation between the flavor of a reconstructed neutral $D$ meson (signal $D$ meson) and the electric charges of particles reconstructed in the rest of the ${e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}c\overline{c}$ event. These include those originating from the decay of the other charm hadron produced in the event, as well as those possibly produced in association with the signal $D$ meson. We develop the algorithm using simulation and calibrate it in data using decay modes that identify the flavor of the decaying neutral $D$ meson. We use a data sample of ${e}^{+}{e}^{\ensuremath{-}}$ collisions, corresponding to $362\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of integrated luminosity, collected by Belle II at center-of-mass energies near the $\mathrm{\ensuremath{\Upsilon}}(4S)$ mass. The effective tagging efficiency in data is $(47.91\ifmmode\pm\else\textpm\fi{}0.07(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}0.51(\mathrm{syst}))%$, independent of the neutral-$D$-meson decay mode. This charm flavor tagger will approximately double the effective sample size of many $CP$-violation and charm-mixing measurements that so far have exclusively relied on neutral $D$ mesons originating from ${D}^{*\ifmmode\pm\else\textpm\fi{}}$ decays. While developed for Belle II, the basic principles underlying the charm flavor tagger can be used in other experiments, including those at hadron colliders.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".