Galaxy formation as a cosmological tool – I. The galaxy merger history as a measure of cosmological parameters
Bibliographic record
Abstract
As galaxy formation and evolution over long cosmic time-scales depends to a large degree on the structure of the universe, the assembly history of galaxies is potentially a powerful approach for learning about the universe itself. In this paper, we examine the merger history of dark matter haloes based on the Extended Press–Schechter formalism as a function of cosmological parameters, redshift and halo mass. We calculate how major halo mergers are influenced by changes in the cosmological values of Ωm, ΩΛ, σ8, the dark matter particle temperature (warm versus cold dark matter), and the value of a constant and evolving equation of state parameter w(z). We find that the merger fraction at a given halo mass varies by up to a factor of 3 for haloes forming under the assumption of cold dark matter, within different underling cosmological parameters. We find that the current measurements of the merger history, as measured through observed galaxy pairs as well as through structure, are in agreement with the concordance cosmology with the current best fit giving |$1 - \Omega _{\rm m} = \Omega _{\rm \Lambda } = 0.84^{+0.16}_{-0.17}$|. To obtain a more accurate constraint competitive with recently measured cosmological parameters from Planck and Wilkinson Microwave Anisotropy Probe requires a measured merger accuracy of δfm ∼ 0.01, implying surveys with an accurately measured merger history over 2–20 deg2, which will be feasible with the next generation of imaging and spectroscopic surveys such as Euclid and LSST.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".