Unique Tracks Drive the Scatter of the Spatially Resolved Star Formation Main Sequence
Bibliographic record
Abstract
Abstract The scatter of the spatially resolved star formation main sequence (SFMS) is investigated in order to reveal signatures about the processes of galaxy formation and evolution. We have assembled a sample of 355 nearby galaxies with spatially resolved Hα and mid-infrared fluxes from the Survey for Ionized Neutral Gas in Galaxies and the Wide-field Infrared Survey Explorer, respectively. We examine the impact of various star formation rate (SFR) and stellar mass transformations on the SFMS. Ranging from 106 to 1011.5 M ⊙ and derived from color to mass-to-light ratio methods for mid-infrared bands, the stellar masses are internally consistent within their range of applicability and inherent systematic errors; a constant mass-to-light ratio also yields representative stellar masses. The various SFR estimates show intrinsic differences and produce noticeable vertical shifts in the SFMS, depending on the timescales and physics encompassed by the corresponding tracer. SFR estimates appear to break down on physical scales below 500 pc. We also examine the various sources of scatter in the spatially resolved SFMS and find morphology does not play a significant role. We identify three unique tracks across the SFMS by individual galaxies, delineated by a critical stellar mass density of log( ) ∼ 7.5. Below this scale, the SFMS shows no clear trend and is likely driven by local, stochastic internal processes. Above this scale, all spatially resolved galaxies have comparable SFMS slopes but exhibit two different behaviors, resulting likely from the rate of mass accretion at the center of the galaxy.
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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 | 0.003 |
| 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 | 0.000 |
| 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".