MusE GAs FLOw and Wind (MEGAFLOW) IX. The impact of gas flows on the relations between the mass, star formation rate, and metallicity of galaxies
Bibliographic record
Abstract
ABSTRACT We study the link between gas flow events and key galaxy scaling relations: the relations between star formation rate (SFR) and stellar mass (the main sequence, MS); gas metallicity and stellar mass (the mass–metallicity relation, MZR); and gas metallicity, stellar mass, and SFR (the fundamental metallicity relation, FMR). Using all star-forming galaxies (SFGs) in the 22 MUSE fields of the MusE GAs FLOw and Wind (MEGAFLOW) survey, we derive the MS, MZR, and FMR scaling relations for 385 SFGs with $M_\star = 10^{8} - 10^{11.5}\, {\rm M}_\odot$ at redshifts 0.35 < z < 0.85. Using the MUSE data and complementary X-Shooter spectra at 0.85 < z < 1.4, we determine the locations of 21 SFGs associated with inflowing or outflowing circumgalactic gas (i.e. with strong Mg ii absorption in background quasar spectra) relative to these scaling relations. Compared to a control sample of galaxies without gas flows (i.e. without Mg ii absorption within 70 kpc of the quasar), SFGs with inflow events (i.e. Mg ii absorption along the major axis) are preferentially located above the MS, while SFGs with outflow events (i.e. Mg ii absorption along the minor axis) are preferentially more metal rich. Our observations support the scenario in which gas accretion increases the SFR while diluting the metal content and where circumgalactic outflows are found in more metal-rich galaxies.
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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 | 0.001 |
| 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) | 0.002 | 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".