Close Major-merger Pairs at <i>z</i> = 0: Star-forming Galaxies with Pseudobulges
Bibliographic record
Abstract
Abstract We present a study of star-forming galaxies (SFGs) with pseudobulges (bulges with Sérsic index n < 2) in a local close major-merger galaxy pair sample (H-KPAIR). With data from new aperture photometries in the optical and near-infrared bands (aperture size of 7 kpc) and from the literature, we find that the mean Age of central stellar populations in Spirals with pseudobulges is consistent with that of disky galaxies and is nearly constant against the bulge-to-total ratio (B/T). Paired Spirals have a slightly lower fraction of pure disk galaxies (B/T ≤ 0.1) than their counterparts in the control sample. Compared to SFGs with classical bulges, those with pseudobulges have a higher (>2σ) mean of specific star formation rate (sSFR) enhancement (sSFRenh = 0.33 ± 0.07 versus sSFRenh = 0.12 ± 0.06) and broader scatter (by ∼1 dex). The eight SFGs that have the highest sSFRenh in the sample all have pseudobulges. A majority (69%) of paired SFGs with strong enhancement (having sSFR more than 5 times the median of the control galaxies) have pseudobulges. The Spitzer data show that the pseudobulges in these galaxies are tightly linked to nuclear/circum-nuclear starbursts. Pseudobulge SFGs in S+S and in S+E pairs have significantly (>3σ) different sSFR enhancement, with the means of sSFRenh = 0.45 ± 0.08 and −0.04 ± 0.11, respectively. We find a decrease in the sSFR enhancements with the density of the environment for SFGs with pseudobulges. Since a high fraction (5/11) of pseudobulge SFGs in S+E pairs are in rich groups/clusters (local density N1Mpc ≥ 7), the dense environment might be the cause for their low sSFRenh.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".