Stokes IQUV magnetic Doppler imaging of Ap stars – III. Next generation chemical abundance mapping of α2 CVn★
Bibliographic record
Abstract
In a previous paper, we presented an updated magnetic field map for the chemically peculiar star α2 CVn using ESPaDOnS and Narval time-resolved high-resolution Stokes IQUV spectra. In this paper, we focus on mapping various chemical element distributions on the surface of α2 CVn. With the new magnetic field map and new chemical abundance distributions, we can investigate the interplay between the chemical abundance structures and the magnetic field topology on the surface of α2 CVn. Previous attempts at chemical abundance mapping of α2 CVn relied on lower resolution data. With our high-resolution (R = 65 000) data set, we present nine chemical abundance maps for the elements O, Si, Cl, Ti, Cr, Fe, Pr, Nd and Eu. We also derive an updated magnetic field map from Fe and Cr lines in Stokes IQUV and O and Cl in Stokes IV. These new maps are inferred from line profiles in Stokes IV using the magnetic Doppler imaging code invers10. We examine these new chemical maps and investigate correlations with the magnetic topology of α2 CVn. We show that chemical abundance distributions vary between elements, with two distinct groups of elements; one accumulates close to the negative part of the radial field, whilst the other group shows higher abundances located where the radial magnetic field is of the order of 2 kG regardless of the polarity of the radial field component. We compare our results with previous works which have mapped chemical abundance structures of Ap stars. With the exception of Cr and Fe, we find no clear trend between what we reconstruct and other mapping results. We also find a lack of agreement with theoretical predictions. This suggests that there is a gap in our theoretical understanding of the formation of horizontal chemical abundance structures and the connection to the magnetic field in Ap stars.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".