Highly improved staggered quarks on the lattice with applications to charm physics
Bibliographic record
Abstract
We use perturbative Symanzik improvement to create a new staggered-quark action (HISQ) that has greatly reduced one-loop taste-exchange errors, no tree-level order ${a}^{2}$ errors, and no tree-level order $(am{)}^{4}$ errors to leading order in the quark's velocity $v/c$. We demonstrate with simulations that the resulting action has taste-exchange interactions that are 3--4 times smaller than the widely used ASQTAD action. We show how to bound errors due to taste exchange by comparing ASQTAD and HISQ simulations, and demonstrate with simulations that such errors are likely no more than 1% when HISQ is used for light quarks at lattice spacings of $1/10\text{ }\text{ }\mathrm{fm}$ or less. The suppression of $(am{)}^{4}$ errors also makes HISQ the most accurate discretization currently available for simulating $c$ quarks. We demonstrate this in a new analysis of the $\ensuremath{\psi}\ensuremath{-}{\ensuremath{\eta}}_{c}$ mass splitting using the HISQ action on lattices where $a{m}_{c}=0.43$ and 0.66, with full-QCD gluon configurations (from MILC). We obtain a result of 111(5) MeV which compares well with the experiment. We discuss applications of this formalism to $D$ physics and present our first high-precision results for ${D}_{s}$ mesons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".