The central spectra of massive star-forming galaxies
Bibliographic record
Abstract
Abstract We have examined the nuclear spectra of very massive star-forming galaxies at z ∼ 0 to understand how they differ from other galaxies with comparable masses, which are typically passive. We selected a sample of 126 nearby massive star-forming galaxies ( < 100 Mpc, 10 11.3 M ⊙ ≤ M stellar ≤ 10 11.7 M ⊙ , 1 M ⊙ yr −1 < SFR < 13 M ⊙ yr −1 ) from the 2MRS-Bright WXSC catalogue. LEDA morphologies indicate at least 63% of our galaxies are spirals, while visual inspection of Dark Energy Survey images reveals 75% of our galaxies to be spirals with the remainder being lenticular. Of our sample 59 have archival nuclear spectra, which we have modelled and subsequently measured emission lines ([NII] λ 6583, H αλ 6563, [OIII] λ 5008, and H βλ 4863), classifying galaxies as star-forming, LINERS or AGNs. Using a BPT diagram we find 83 ± 6 % of our galaxies, with sufficient signal-to-noise to measure all 4 emission lines, to be LINERs. Using the [NII] λ 6583/H αλ 6563 emission line ratio alone we find that 79 ± 6 % of the galaxies (46 galaxies) with archival spectra are LINERs, whereas just ∼ 30% of the overall massive galaxy population are LINERs (Belfiore et al., 2016). Our sample can be considered a local analogue of the Ogle et al. (2016, 2019) sample of z ∼ 0.22 massive star-forming galaxies in terms of selection criteria, and we find 64% of their galaxies are LINERs using SDSS spectra. The high frequency of LINER emission in these massive star-forming galaxies indicates that LINER emission in massive galaxies may be linked to the presence of gas that fuels star formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".