Improving Photometric Redshifts of Epoch of Reionization Galaxies: A New Empirical Transmission Curve with Neutral Hydrogen Damping Wing Ly<i>α</i> Absorption
Bibliographic record
Abstract
Abstract We present a new analytical model for the attenuation to Epoch of Reionization (EoR) galaxies by proximate neutral hydrogen gas. Many galaxy spectra in the EoR taken by JWST have shown a flux deficit at wavelengths just redward of the Lyman break, and this has been regarded as resulting from Lyα damping wing absorption by the increasing amount of neutral hydrogen in the line of sight. However, previous attenuation models for the intergalactic medium (IGM) commonly used in photometric redshift template-fitting codes assume that the Lyman break is rather sharp, which leads to systematic overestimation of photometric redshifts at z > 7. In this Letter, we build and empirically calibrate a new attenuation model that takes the increased Lyα damping wing absorption into account. Our model consists of the canonical IGM absorption and an additional absorption component due to dense neutral hydrogen gas clouds proximate to the galaxy, and we derive the redshift evolution of H i column density of the proximate clouds by calibrating the model using CANUCS JWST observations. The resulting total transmission curve resolves the photometric redshift bias at z > 7, an improvement that is robust to the choice of template-fitting code, template set, and photometric catalog used. The new attenuation model can be easily implemented in existing template-fitting codes and significantly improves the photometric redshift performance in the EoR.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".