Connecting stellar mass and star-formation rate to dark matter halo mass out to z ∼ 2
Bibliographic record
Abstract
We have constructed an extended halo model (EHM) which relates the total stellar mass and star-formation rate (SFR) to halo mass (Mh). An empirical relation between the distribution functions of total stellar mass of galaxies and host halo mass, tuned to match the spatial density of galaxies over 0 < z < 2 and the clustering properties at z ∼ 0, is extended to include two different scenarios describing the variation of SFR on Mh. We also present new measurements of the redshift evolution of the average SFR for star-forming galaxies of different stellar masses up to z = 2, using data from the Herschel Multi-tiered Extragalactic Survey for infrared bright galaxies. Combining the EHM with the halo accretion histories from numerical simulations, we trace the stellar mass growth and star-formation history in haloes spanning a range of masses. We find that: (1) the intensity of the star-forming activity in haloes in the probed mass range has steadily decreased from z ∼ 2 to 0; (2) at a given epoch, haloes in the mass range between a few times 1011 M⊙ and a few times 1012 M⊙ are the most efficient at hosting star formation; (3) the peak of SFR density shifts to lower mass haloes over time; and (4) galaxies that are forming stars most actively at z ∼ 2 evolve into quiescent galaxies in today's group environments, strongly supporting previous claims that the most powerful starbursts at z ∼ 2 are progenitors of today's elliptical 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".