Detection of Cosmological 21 cm Emission with the Canadian Hydrogen Intensity Mapping Experiment
Bibliographic record
Abstract
We present a detection of 21-cm emission from large-scale structure (LSS) between redshift 0.78 and 1.43 made with the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Radio observations acquired over 102 nights are used to construct maps which are foreground filtered and stacked on the angular and spectral locations of luminous red galaxies (LRG), emission line galaxies (ELG), and quasars (QSO) from the eBOSS clustering catalogs. We find decisive evidence for a detection when stacking on all three tracers of LSS, with the logarithm of the Bayes Factor equal to 18.9 (LRG), 10.8 (ELG), and 56.3 (QSO). An alternative frequentist interpretation, based on the likelihood-ratio test, yields a detection significance of $7.1σ$ (LRG), $5.7σ$ (ELG), and $11.1σ$ (QSO). These are the first 21-cm intensity mapping measurements made with an interferometer. We constrain the effective clustering amplitude of neutral hydrogen (HI), defined as $\mathcal{A}_{\rm HI}\equiv 10^{3}\,Ω_\mathrm{HI}\left(b_\mathrm{HI}+\langle\,fμ^{2}\rangle\right)$, where $Ω_\mathrm{HI}$ is the cosmic abundance of HI, $b_\mathrm{HI}$ is the linear bias of HI, and $\langle\,fμ^{2}\rangle=0.552$ encodes the effect of redshift-space distortions at linear order. We find $\mathcal{A}_\mathrm{HI}=1.51^{+3.60}_{-0.97}$ for LRGs $(z=0.84)$, $\mathcal{A}_\mathrm{HI}=6.76^{+9.04}_{-3.79}$ for ELGs $(z=0.96)$, and $\mathcal{A}_\mathrm{HI}=1.68^{+1.10}_{-0.67}$ for QSOs $(z=1.20)$, with constraints limited by modeling uncertainties at nonlinear scales. We are also sensitive to bias in the spectroscopic redshifts of each tracer, and find a non-zero bias $Δ\,v= -66 \pm 20 \mathrm{km/s}$ for the QSOs. We split the QSO catalog into three redshift bins and have a decisive detection in each, with the upper bin at $z=1.30$ producing the highest redshift 21-cm intensity mapping measurement thus far.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".