REIONIZATION HISTORIES OF MILKY WAY MASS HALOS
Bibliographic record
Abstract
We investigate the connection between the reionization era and the present-day universe by examining the mass reionization histories of z = 0 dark matter halos. In a 600 3 Mpc 3 volume, we combine a dark matter N -body simulation with a three-dimensional seminumerical reionization model. This tags each particle with a reionization redshift, so that individual present-day halos can be connected to their reionization histories and environments. We find that the vast majority of present-day halos with masses larger than ∼ few × 10 11 M ☉ reionize earlier than the rest of the universe. We also find significant halo-to-halo diversity in mass reionization histories, and find that in realistic inhomogeneous models, the material within a given halo is not expected to reionize at the same time. In particular, the scatter in reionization times within individual halos is typically larger than the scatter among halos. From our fiducial reionization model, we find that the typical 68% scatter in reionization times within halos is ∼115 Myr for 10 12 ± 0.25 M ☉ halos, decreasing slightly to ∼95 Myr for 10 15 ± 0.25 M ☉ halos. We find a mild correlation between reionization history and environment: halos with shorter reionization histories are typically in more clustered environments, with the strongest trend on a scale of ∼20 Mpc. Material in Milky Way mass halos with short reionization histories is preferentially reionized in relatively large H ii regions, implying reionization mostly by sources external to the progenitors of the present-day halo. We investigate the impact on our results of varying the reionization model parameters, which span a range of reionization scenarios with varying timing and morphology.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".