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, 1011.3 M⊙ ≤ Mstellar ≤ 1011.7 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]λ6583, Hαλ6563, [OIII]λ5008, and Hβλ4863), 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]λ6583/Hαλ6563 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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".