The re-markable 21-cm power spectrum. Part I. Probing the <scp>Hi</scp> distribution in the post-reionization era using marked statistics
Bibliographic record
Abstract
Abstract The neutral hydrogen (Hi) power spectrum, measured from intensity fluctuations in the 21-cm background, offers insights into the large-scale structures (LSS) of our Universe in the post-reionization era (redshift z < 6). A significant amount of Hi is expected to reside in low- and intermediate-density environments, but the power spectrum mainly captures information from high-density regions. To more fully extract the information contained in the Hi field, we investigate the use of a marked power spectrum statistic. Here, the power spectrum is effectively re-weighted using a non-linear mark function which depends on the smoothed local density, such that low- or high-density regions are up- or down-weighted. This approach may also capture information on some higher-order statistical moments of the field. We model the Hi distribution using semi-numerical simulations and for the first time study the marked Hi power spectrum, across 1 ≤ z ≤ 5. Our analysis indicates that there is considerable evolution of the Hi field during the post-reionization era. Over a wide range of length scales (comoving wave numbers 0.05 ≤ k ≤ 1.0 Mpc-1) we expectedly find that the Hi evolves slowly at early times, but more rapidly at late times. This evolution is not well-captured by the power spectrum of the standard (unmarked) Hi field. We also study how the evolution of the Hi field depends on the chosen smoothing scale for the mark, and how this affects the marked power spectrum. We conclude that the information about the Hi content at low and intermediate densities is important for a correct and consistent analysis of Hi content and evolution based on the 21-cm background.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".