Imaging neutral hydrogen on large scales during the Epoch of Reionization with LOFAR
Bibliographic record
Abstract
The first generation of redshifted 21 cm detection experiments, carried out with arrays like Low Frequency Array (LOFAR), Murchison Widefield Array (MWA) and Giant Metrewave Telescope (GMRT), will have a very low signal-to-noise ratio (S/N) per resolution element (≲0.2). In addition, whereas the variance of the cosmological signal decreases on scales larger than the typical size of ionization bubbles, the variance of the formidable galactic foregrounds increases, making it hard to disentangle the two on such large scales. The poor sensitivity on small scales, on the one hand, and the foregrounds effect on large scales, on the other hand, make direct imaging of the Epoch of Reionization of the Universe very difficult, and detection of the signal therefore is expected to be statistical. Despite these hurdles, in this paper we argue that for many reionization scenarios low-resolution images could be obtained from the expected data. This is because at the later stages of the process one still finds very large pockets of neutral regions in the intergalactic medium, reflecting the clustering of the large-scale structure, which stays strong up to scales of ≈120 h−1 comoving Mpc (≈1°). The coherence of the emission on those scales allows us to reach sufficient S/N (≳3) so as to obtain reionization 21 cm images. Such images will be extremely valuable for answering many cosmological questions but above all they will be a very powerful tool to test our control of the systematics in the data. The existence of this typical scale (≈120 h−1 comoving Mpc) also argues for designing future EoR experiments, e.g. with Square Kilometre Array, with a field of view of at least 4°.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".