Are all starbursts equal? Star-formation-rate profiles in merger versus secular starbursts
Bibliographic record
Abstract
Aims. Galaxy interactions can trigger drastic changes in the resolved star-forming properties of their constituents, but it remains unclear as to whether those changes are discernible from secular starburst triggers. In this Letter we investigate whether or not post-merger galaxies create unique star-forming trends on a kiloparsec scale. Methods. We present radial trends in star-formation-rate (SFR) surface density (ΣSFR) for 150 post-merger galaxies with moderate to extremely heightened global SFRs using observations from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. We juxtapose these profiles with those of noninteracting galaxies (excluding both galaxy pairs and post-merger galaxies) with similarly enhanced global SFRs. Results. Post-merger galaxies have a much stronger central starburst than isolated galaxies with similar global star-formation enhancements. Indeed, isolated starburst galaxies (SBs) lack a marked central enhancement and instead show a fairly uniform enhancement in ΣSFR with radius. Moreover, the difference in central star formation between post-merger galaxies and noninteracting galaxies is more radially extended and pronounced when the global enhancement in star formation is larger. We conclude that post-merger galaxies create a unique signature in their resolved star-forming properties that is distinct from secular processes that can trigger similar global SFR enhancements.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".