The effect of mass and morphology on the mass assembly of galaxies
Bibliographic record
Abstract
The pace at which galaxies grew into their current stellar masses and how this growth is regulated is still not fully understood, nor is the role that morphology plays in this process. We applied full spectral fitting techniques with pyPipe3D to the MaNGA sample to obtain its star formation and stellar mass histories and used these to investigate the mass assembly of galaxies by measuring how their specific star formation correlates to their stellar mass at different look-back times. We find that the correlation between these two parameters was shallower in the past. Galaxies used to have similar mass doubling times and the current negative correlation between the specific star formation and M* is primarily due to more massive galaxies ‘dropping’ off the main sequence earlier than less massive ones. Additionally, selecting the galaxies into bins based on their present-day morphology shows a segregation in specific star formation rate (sSFR) that is maintained even at high look-back times, showing that the factors that determine which morphology a galaxy ends up in are in place at very early times. Similarly, selecting them based on their current star formation status shows that, on average, currently retired galaxies used to have slightly a higher sSFR before the drop-off, whereas galaxies that have continued to form stars until today had a lower sSFR initially. We compare our results to a set of cosmic surveys, finding partial agreement in our results with several of them, though with significant offsets in redshift. Finally, we discuss how our results fit with certain theoretical models on galaxy evolution as well as cosmological simulations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".