The Mass Growth and Stellar Ages of Galaxies: Observations versus Simulations
Bibliographic record
Abstract
Using observed stellar mass functions out to $z=5$, we measure the main progenitor stellar mass growth of descendant galaxies with masses of $\log{M_{*}/M_{\odot}}=11.5,11.0,10.5,10.0$ at $z\sim0.1$ using an evolving cumulative number density selection. From these mass growth histories, we are able to measure the time at which half the total stellar mass of the descendant galaxy was assembled, $t_{a}$, which, in order of decreasing mass corresponds to redshifts of $z_{a}=1.28, 0.92, 0.60$ and $0.51$. We compare this to the median light-weighted stellar age $t_{*}$ ($z_{*} = 2.08, 1.49, 0.82$ and $0.37$) of a sample of low redshift SDSS galaxies (from the literature) and find the timescales are consistent with more massive galaxies forming a higher fraction of their stars ex-situ compared to lower mass descendants. We find that both $t_{*}$ and $t_{a}$ strongly correlate with mass which is in contrast to what is found in the EAGLE hydrodynamical simulation which shows a flat relationship between $t_{a}$ and $M_{*}$. However, the semi-analytic model of \citet{henriques2015} is consistent with the observations in both $t_{a}$ and $t_{*}$ with $M_{*}$, showing the most recent semi-analytic models are better able to decouple the evolution of the baryons from the dark matter in lower-mass galaxies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".