Determination of <i>z</i> ∼ 0.8 neutral hydrogen fluctuations using the 21 cm intensity mapping autocorrelation
Bibliographic record
Abstract
Abstract The large-scale distribution of neutral hydrogen in the Universe will be luminous through its 21 cm emission. Here, for the first time, we use the auto-power spectrum of 21 cm intensity fluctuations to constrain neutral hydrogen fluctuations at z ∼ 0.8. Our data were acquired with the Green Bank Telescope and span the redshift range 0.6 < z < 1 over two fields totalling ≈41 deg2 and 190 h of radio integration time. The dominant synchrotron foregrounds exceed the signal by ∼103, but have fewer degrees of freedom and can be removed efficiently. Even in the presence of residual foregrounds, the auto-power can still be interpreted as an upper bound on the 21 cm signal. Our previous measurements of the cross-correlation of 21 cm intensity and the WiggleZ galaxy survey provide a lower bound. Through a Bayesian treatment of signal and foregrounds, we can combine both fields in auto- and cross-power into a measurement of ΩHI bHI= [0.62+0.23−0.15] × 10−3 at 68 per cent confidence with 9 per cent systematic calibration uncertainty, where ΩHI is the neutral hydrogen (H i) fraction and bHI is the H i bias parameter. We describe observational challenges with the present data set and plans to overcome them.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".