Signatures of reionization feedback in the near-infrared background
Bibliographic record
Abstract
ABSTRACT The reionization of the intergalactic medium at redshifts z ≳ 6 is expected to have a lasting impact on galaxies residing in low-mass dark matter haloes. Unable to accrete or retain gas photoheated to temperatures T ≳ 104 K, the star formation histories of faint galaxies in the early Universe are expected to decline as they exhaust their gas supply, and so give rise to a ‘turnover’ at the faint-end of the galaxy luminosity function. In this work, we explore the possibility of constraining this reionization feedback with measurements of the cosmic near-infrared background (NIRB), which traces the rest-optical and ultraviolet emission of high-redshift galaxies. We find that the contrast between passively-ageing low-mass galaxies quenched by reionization and bluer actively star-forming galaxies unaffected by reionization, manifests as a scale- and colour-dependent signature in the NIRB at a level comparable to the sensitivity of NASA’s upcoming SPHEREx mission. Whereas models with pure mass suppression largely affect the signal at wavelengths ≲2 μm, ∼5 per cent-level differences in the background persist out to ≃5 μm for reionization feedback models on ≃20 arcmin scales. Finally, the power spectra of intensity ratio maps exhibit larger ∼ tens of per cent variations, and may thus be a promising target for future analyses.
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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) | 0.001 | 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".