Time-scales for the effects of interactions on galaxy properties and SMBH growth
Bibliographic record
Abstract
ABSTRACT Galaxy interaction and merging have clear effects on the systems involved. We find an increase in the star formation rate (SFR), potential ignition of active galactic nuclei (AGNs), and significant morphology changes. However, at what stage during interactions or mergers these changes begin to occur remains an open question. With a combination of machine learning and visual classification, we select a sample of 3162 interacting and merging galaxies in the Cosmic Evolutionary Survey (COSMOS) field across a redshift range of 0.0–1.2. We divide this sample into four distinct stages of interaction based on their morphology, each stage representing a different phase of the dynamical time-scale. We use the rich ancillary data available in COSMOS to probe the relation between interaction stage, stellar mass, SFR, and AGN fraction. We find that the distribution of SFRs rapidly changes with stage for mass distributions consistent with being drawn from the same parent sample. This is driven by a decrease in the fraction of red sequence galaxies (from 17 per cent as close pairs to 1.4 per cent during merging) and an increase in the fraction of starburst galaxies (from 7 per cent to 32 per cent). We find that the AGN fraction increases by a factor of 1.2 only at coalescence. We find that the effects of interaction peak at the point of closest approach and coalescence of the two systems. We show that the point in time of the underlying dynamical time-scale – and its related morphology – is as important to consider as its projected separation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".