Spatially resolved star formation and metallicity profiles in post-merger galaxies from MaNGA
Bibliographic record
Abstract
ABSTRACT Large galaxy surveys have demonstrated that galaxy–galaxy mergers can dramatically change the morphologies, star formation rates (SFRs), and metallicities of their constituents. However, most statistical studies have been limited to the measurement of global quantities, through large fibres or integrated colours. In this work, we present the first statistically significant study of spatially resolved star formation and metallicity profiles using integral field spectroscopy, using a sample of ∼20 000 spaxels in 36 visually selected post-merger galaxies from the Mapping Nearby Galaxies at Apache Point Observatory survey. By measuring offsets from SFR and metallicity scaling relations on a spaxel-by-spaxel basis, we are able to quantify where in the galaxy these properties are most affected by the interaction. We find that the SFR enhancements are generally centrally peaked, by a factor of 2.5 on average, in agreement with predictions from simulations. However, there is considerable variation in the SFR behaviour in the galactic outskirts, where both enhancement and suppression are seen. The median SFR remains enhanced by 0.1 dex out to at least 1.9 Re. The metallicity is also affected out to these large radii, typically showing a suppression of ∼−0.04 dex.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".