The Stellar Mass Tully-Fisher Relation to <i>z</i> = 1.2 from AEGIS
Bibliographic record
Abstract
We combine newly measured rotation velocities, velocity dispersions, and stellar masses to construct stellar mass Tully-Fisher relations (M * TFRs) for 544 galaxies with strong emission lines at 0.1 < z < 1.2 from the All-Wavelength Extended Groth Strip International Survey (AEGIS) and the Deep Extragalactic Evolutionary Probe 2 (DEEP2) survey. The conventional M * TFR using only rotation velocity ( V rot ) shows large scatter (~1.5 dex in velocity). The scatter and residuals are correlated with morphology in the sense that disturbed, compact, and major merger galaxies have lower velocities for their masses. We construct an M * TFR using the kinematic estimator S 0.5 , which is defined as 1/2 and accounts for disordered or noncircular motions through the gas velocity dispersion (σ g ). The new M * TFR, termed S 0.5 /M * TFR, is remarkably tight over 0.1 < z < 1.2, with no detectable evolution of its intercept or slope with redshift. The average best-fit relation has 0.47 dex scatter in stellar mass, corresponding to ~1.2 "magnitudes," assuming a constant mass-to-light ratio. Interestingly, the S 0.5 /M * TFR is consistent with the absorption-line-based stellar mass Faber-Jackson relation for nearby elliptical galaxies in terms of slope and intercept, which might suggest a physical connection between the two relations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".