H <scp>i</scp> constraints from the cross-correlation of eBOSS galaxies and Green Bank Telescope intensity maps
Bibliographic record
Abstract
ABSTRACT We present the joint analysis of Neutral Hydrogen (H i) Intensity Mapping observations with three galaxy samples: the Luminous Red Galaxy (LRG) and Emission Line Galaxy (ELG) samples from the eBOSS survey, and the WiggleZ Dark Energy Survey sample. The H i intensity maps are Green Bank Telescope observations of the redshifted $21\rm cm$ emission on $100 \, {\rm deg}^2$ covering the redshift range 0.6 < z < 1.0. We process the data by separating and removing the foregrounds present in the radio frequencies with FastI ICA. We verify the quality of the foreground separation with mock realizations, and construct a transfer function to correct for the effects of foreground removal on the H i signal. We cross-correlate the cleaned H i data with the galaxy samples and study the overall amplitude as well as the scale dependence of the power spectrum. We also qualitatively compare our findings with the predictions by a semianalytical galaxy evolution simulation. The cross-correlations constrain the quantity $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm opt}}$ at an effective scale keff, where $\Omega _\rm {H\,\small {I}}$ is the H i density fraction, $b_\rm {H\,\small {I}}$ is the H i bias, and $r_{\rm {H\,\small {I}},{\rm opt}}$ the galaxy–hydrogen correlation coefficient, which is dependent on the H i content of the optical galaxy sample. At $k_{\rm eff}=0.31 \, h\,{\rm Mpc^{-1}}$ we find $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm Wig}} = [0.58 \pm 0.09 \, {\rm (stat) \pm 0.05 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-WiggleZ, $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm ELG}} = [0.40 \pm 0.09 \, {\rm (stat) \pm 0.04 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-ELG, and $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm LRG}} = [0.35 \pm 0.08 \, {\rm (stat) \pm 0.03 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-LRG, at z ≃ 0.8. We also report results at $k_{\rm eff}=0.24$ and $k_{\rm eff}=0.48 \, h\,{\rm Mpc^{-1}}$. With little information on H i parameters beyond our local Universe, these are amongst the most precise constraints on neutral hydrogen density fluctuations in an underexplored redshift range.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".