ACACIAS I: element abundance labels for 192 stars in the dwarf galaxy NGC 6822
Bibliographic record
Abstract
ABSTRACT The element abundances of local group galaxies connect enrichment mechanisms to galactic properties and serve to contextualize the Milky Way’s abundance distributions. Individual stellar spectra in nearby galaxies can be extracted from integral field unit (IFU) data, and provide a means to take an abundance census of the local group. We introduce a programme that leverages $R=1800$, $\mathrm{SNR}=15$, IFU resolved spectra from the multi unit spectroscopic explorer . We deploy the data-driven modelling approach for labelling stellar spectra with stellar parameters and abundances, of The Cannon, on resolved stars in NGC 6822. We construct our model for The Cannon using $\approx$19 000 Milky Way lamost spectra with apogee labels. We report six inferred abundance labels (denoted $\ell _\mathrm{X}$), for 192 NGC 6822 disc stars, precise to $\approx$0.15 dex. We validate our generated spectral models provide a good fit to the data, including at individual atomic line features. We infer mean abundances of $\ell _\mathrm{[Fe/H]} = -0.90 \pm 0.03$, $\ell _\mathrm{[Mg/Fe]} = -0.01 \pm 0.01$, $\ell _\mathrm{[Mn/Fe]} = -0.22 \pm 0.02$, $\ell _\mathrm{[Al/Fe]} = -0.33 \pm 0.03$, $\ell _\mathrm{[C/Fe]} =-0.43 \pm 0.03$, $\ell _\mathrm{[N/Fe]} =0.18 \pm 0.03$. These abundance labels are similar to those of dwarf galaxies observed by apogee, and the lower enhancements for NGC 6822 compared to the Milky Way are consistent with expectations. This approach supports a new era in extragalactic archaeology of characterizing the local group enrichment diversity using low-resolution, low signal to noise ratio IFU resolved spectra. Furthermore, we conclude that it is feasible to build a model based on spectra observed with one instrument and apply it to spectra obtained with another.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".