JADES: Detecting [OIII]<i>λ</i>4363 emitters and testing strong line calibrations in the high-<i>z</i> Universe with ultra-deep JWST/NIRSpec spectroscopy up to <i>z</i> ∼ 9.5
Bibliographic record
Abstract
We present ten novel [OIII]λ4363 auroral line detections up to z ∼ 9.5 measured from ultra-deep JWST/NIRSpec MSA spectroscopy from the JWST Advanced Deep Extragalactic Survey (JADES). We leverage the deepest spectroscopic observations taken thus far with NIRSpec to determine electron temperatures and oxygen abundances using the direct Te method. We directly compare these results against a suite of locally calibrated strong-line diagnostics and recent high-z calibrations. We find the calibrations fail to simultaneously match our JADES sample, thus warranting a self-consistent revision of these calibrations for the high-z Universe. We find a weak dependence between R2 and O3O2 with metallicity, thus suggesting these line ratios are inefficient in the high-z Universe as metallicity diagnostics and degeneracy breakers. We find R3 and R23 are still correlated with metallicity, but we find a tentative flattening of these diagnostics, thus suggesting future difficulties when applying these strong line ratios as metallicity indicators in the high-z Universe. We also propose and test an alternative diagnostic based on a different combination of R3 and R2 with a higher dynamic range. We find a reasonably good agreement (median offset of 0.002 dex, median absolute offset of 0.13 dex) with the JWST sample at low metallicity, but future investigations are required on larger samples to probe past the turnover point. At a given metallicity, our sample demonstrates higher ionization and excitation ratios than local galaxies with rest-frame EWs(Hβ) ≈200 − 300 Å. However, we find the median rest-frame EWs(Hβ) of our sample to be ∼2× less than the galaxies used for the local calibrations. This EW discrepancy combined with the high ionization of our galaxies does not offer a clear description of [OIII]λ4363 production in the high-z Universe, thus warranting a much deeper examination into the factors influencing these processes.
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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 | 0.001 |
| 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 | 0.001 |
| 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".