LESS THAN 10 PERCENT OF STAR FORMATION IN<i>z</i>∼ 0.6 MASSIVE GALAXIES IS TRIGGERED BY MAJOR INTERACTIONS
Bibliographic record
Abstract
Both observations and simulations show that major tidal interactions or mergers between gas-rich galaxies can lead to intense bursts of star formation. Yet, the average enhancement in star formation rate (SFR) in major mergers and the contribution of such events to the cosmic SFR are not well estimated. Here we use photometric redshifts, stellar masses, and UV SFRs from COMBO-17, 24 μm SFRs from Spitzer, and morphologies from two deep Hubble Space Telescope (HST) cosmological survey fields (ECDFS/GEMS and A901/STAGES) to study the enhancement in SFR as a function of projected galaxy separation. We apply two-point projected correlation function techniques, which we augment with morphologically selected very close pairs (separation <2″) and merger remnants from the HST imaging. Our analysis confirms that the most intensely star-forming systems are indeed interacting or merging. Yet, for massive (M * ≥ 1010 M ⊙) star-forming galaxies at 0.4 < z < 0.8, we find that the SFRs of galaxies undergoing a major interaction (mass ratios ≤1:4 and separations ≤40 kpc) are only 1.80 ± 0.30 times higher than the SFRs of non-interacting galaxies when averaged over all interactions and all stages of the interaction, in good agreement with other observational works. Our results also agree with hydrodynamical simulations of galaxy interactions, which produce some mergers with large bursts of star formation on 100 Myr timescales, but only a modest SFR enhancement when averaged over the entire merger timescale. We demonstrate that these results imply that only ≲10% of star formation at 0.4 ≤ z ≤ 0.8 is triggered directly by major mergers and interactions; these events are not important factors in the build-up of stellar mass since z = 1.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".