Calibration-based abundances in the interstellar gas of galaxies from slit and IFU spectra
Bibliographic record
Abstract
In this work, we make use of available integral field unit (IFU) spectroscopy and slit spectra of several nearby galaxies. The pre-existing empirical R and S calibrations for abundance determinations are constructed using a sample of H II regions with high-quality slit spectra. In this paper, we test the applicability of those calibrations to the IFU spectra. We estimate the calibration-based abundances obtained using both the IFU and the slit spectroscopy for eight nearby galaxies. The median values of the slit and IFU spectra-based abundances in bins of 0.1 in fractional radius Rg (normalised to the optical radius R25) of a galaxy are determined and compared. We find that the IFU and the slit spectra-based abundances obtained through the R calibration are close to each other; the mean value of the differences of abundances is 0.005 dex, and the scatter in the differences is 0.037 dex for 38 datapoints. The S calibration can produce systematically underestimated values of the IFU spectra-based abundances at high metallicities (12 + log(O/H) ≳8.55); the mean value of the differences is −0.059 dex for 21 datapoints, while at lower metallicities the mean value of the differences is −0.018 dex and the scatter is 0.045 dex for 36 data points. This provides evidence that the R calibration produces more consistent abundance estimations between the slit and the IFU spectra than the S calibration. We find that the same calibration can produce close estimations of the abundances using IFU spectra obtained with different spatial resolution and different spatial samplings. This is in line with the recent finding that the contribution of the diffuse ionised gas to the large-aperture spectra of H II regions has a secondary effect.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".