Simulation of Primordial Object Formation
Bibliographic record
Abstract
We have included the chemical rate network responsible for the formation of molecular hydrogen in the N -body hydrodynamic code, HYDRA, in order to study the formation of the first cosmological objects at redshifts between 10 and 50. We have tested our implementation of the chemical and cooling processes by comparing N -body top-hat simulations with theoretical predictions from a semianalytic model and found them to be in good agreement. We find that postvirialization properties are insensitive to the initial abundance of H 2 . Our main objective was to determine the minimum mass [ M SG ( z )] of perturbations that could become self-gravitating (a prerequisite for star formation), and the redshift at which this occurred. We have developed a robust indicator for detecting the presence of a self-gravitating cloud in our simulations, and find that we can do so with a baryonic particle mass resolution of 40 M ☉ . We have performed cosmological simulations of primordial objects, and find that the object's mass and redshift at which they become self-gravitating agree well with the M SG ( z ) results from the top-hat simulations. Once a critical H 2 fractional abundance of ~5 × 10 -4 has formed in an object, the cooling time drops below the dynamical time at the center of the cloud and the gas free falls in the dark matter potential wells, becoming self-gravitating a dynamical time later.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".