Dry Mergers in GEMS: The Dynamical Evolution of Massive Early‐Type Galaxies
Bibliographic record
Abstract
We have used the 28' × 28' Hubble Space Telescope image mosaic from the GEMS (Galaxy Evolution from Morphology and SEDs) survey in conjunction with the COMBO-17 photometric redshift survey to constrain the incidence of major mergers between spheroid-dominated galaxies with little cold gas (dry mergers) since z = 0.7. A set of N -body merger simulations was used to explore the morphological signatures of such interactions: they are recognizable either as <5 kpc separation close pairs or because of broad, low surface brightness tidal features and asymmetries. Data with the depth and resolution of GEMS are sensitive to dry mergers between galaxies with M V ≲ -20.5 for z ≲ 0.7; dry mergers at higher redshifts are not easily recovered in single-orbit HST imaging. Six dry mergers (12 galaxies) with luminosity ratios between 1 : 1 and 4 : 1 were found from a sample of 379 red early-type galaxies with M V < -20.5 and 0.1 < z < 0.7. The simulations suggest that the morphological signatures of dry merging are visible for ~150 Myr, and we use this timescale to convert the observed merger incidence into a rate. On this basis we find that present-day spheroidal galaxies with M V < -20.5 on average have undergone between 0.5 and 2 major dry mergers since z ~ 0.7. We have compared this result with the predictions of a cold dark matter-based semianalytic galaxy formation model. The model reproduces the observed declining major merger fraction of bright galaxies and the space density of luminous early-type galaxies reasonably well. The predicted dry merger fraction is consistent with our observational result. Hence, hierarchical models predict and observations now show that major dry mergers are an important driver of the evolution of massive early-type galaxies in recent epochs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".