Deep Extragalactic VIsible Legacy Survey (DEVILS): the sSFR–<i>M</i>⋆ plane. Part I: the recent SFH of galaxies and movement through the plane
Bibliographic record
Abstract
ABSTRACT In a recent paper, we parametrized the evolution of the star formation rate dispersion ($\sigma _{\mathrm{ SFR}}$) across the specific star formation rate–stellar mass plane (sSFR–M$_{\star }$) using the Deep Extragalactic VIsible Legacy Survey (DEVILS) – suggesting that the point at which the minimum in the dispersion occurs (M$^{*}_{\sigma -\mathrm{ min}}$) defines a boundary between different physical mechanisms affecting galaxy evolution. Here, we expand upon that work to determine the movement of galaxies through the sSFR–M$_{\star }$ plane using their recent star formation histories (SFHs) and explore how this leads to the observed $\sigma _{\rm {SFR}}$–M$_{\star }$ relation. We find that galaxies in subregions of the sSFR–M$_{\star }$ plane show distinctly different SFHs, leading to a complex evolution of the sSFR–M$_{\star }$ plane and star-forming sequence (SFS). However, we find that selecting galaxies based on stellar mass and position relative to SFS alone (as is traditionally the case), may not identify sources with common recent SFHs, and therefore propose a new selection methodology. We then use the recent SFH of galaxies to measure the evolution of the SFS, showing that it has varying contributions from galaxies with different SFHs that lead to the observed changes in slope, normalization, and turnover stellar mass. Finally, we determine the overall evolution of the sSFR–M$_{\star }$ plane from $z\sim 1$ to today. In the second paper in this series, we will discuss physical properties of galaxies with common recent SFHs and how these lead to the observed $\sigma _{\rm {SFR}}$–M$_{\star }$ relation and evolution of the sSFR–M$_{\star }$ plane.
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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 | 0.000 | 0.000 |
| 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".