Mock observations with the Millennium Simulation: cosmological downsizing and intermediate-redshift observations
Bibliographic record
Abstract
Only by incorporating various forms of feedback can theories of galaxy formation reproduce the present-day luminosity function of galaxies. It has also been argued that such feedback processes might explain the counterintuitive behaviour of ‘downsizing’ witnessed since redshifts z≃ 1–2. To examine this question, observations spanning 0.4 < z < 1.4 from the Deep Extragalactic Evolutionary Probe (DEEP)2/Palomar survey are compared with a suite of equivalent mock observations derived from the Millennium Simulation, populated with galaxies using the galform code. Although the model successfully reproduces the observed total mass function and the general trend of ‘downsizing’, it fails to accurately reproduce the colour distribution and type-dependent mass functions at all redshifts probed. This failure is shared by other semi-analytical models which collectively appear to ‘over-quench’ star formation in intermediate-mass systems. These mock lightcones are also a valuable tool for investigating the reliability of the observational results in terms of cosmic variance. Using variance estimates derived from the lightcones, we confirm the significance of the decline since z∼ 1 in the observed number density of massive blue galaxies which, we argue, provides the bulk of the associated growth in the red sequence. We also assess the limitations arising from cosmic variance in terms of our ability to observe mass-dependent growth since z∼ 1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".