Intensity Mapping of Hα, Hβ, , and Lines at z < 5
Bibliographic record
Abstract
Abstract Intensity mapping is becoming a useful tool to study the large-scale structure of the universe through spatial variations in the integrated emission from galaxies and the intergalactic medium. We study intensity mapping of the , [O iii] 5007 Å, [O ii] 3727 Å, and lines at . The mean intensities of these four emission lines are estimated using the observed luminosity functions (LFs), cosmological simulations, and the star formation rate density (SFRD) derived from observations at . We calculate the intensity power spectra and consider the foreground contamination of other lines at lower redshifts. We use the proposed NASA small explorer SPHEREx (the Spectro-Photometer for the History of the universe, Epoch of Reionization, and Ices Explorer) as a case study for the detectability of the intensity power spectra of the four emission lines. We also investigate the cross-correlation with the 21 cm line probed by the Canadian Hydrogen Intensity Mapping Experiment (CHIME), Tianlai experiment and the Square Kilometer Array (SKA) at . We find both the auto and cross power spectra can be well measured for the Hα, [O iii] and [O ii] lines at , while it is more challenging for the Hβ line. Finally, we estimate the constraint on the SFRD from intensity mapping, and find we can reach an accuracy higher than 7% at , which is better than with the usual method of measurements using the LFs of galaxies.
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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 | 0.000 | 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) | 0.001 | 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".