A cautionary tale of Ly C escape fraction estimates from high-redshift galaxies
Bibliographic record
Abstract
ABSTRACT Measuring the escape fraction, fesc, of ionizing, Lyman continuum (Ly C) radiation is key to our understanding of the process of cosmic reionization. In this paper, we provide a methodology for recovering the posterior probability distribution of the Ly C escape fraction, $f_{\rm esc}^{\rm PDF}$, considering both the observational uncertainties and ensembles of simulated transmission functions through the intergalactic medium (IGM). We present an example of this method applied to a VUDS galaxy at z = 3.64 and find $f_{\rm esc}^{\rm PDF}$ = 0.51$^{+0.33}_{-0.34}$ and compare this to the values computed assuming averaged IGM transmission with and without consideration of detection bias along average sightlines yielding $f_{\rm esc}^{\langle T \rangle }$ = 1.40$^{+0.80}_{-0.42}$ and $f_{\rm esc}^{\rm bias}$ = 0.82$^{+0.33}_{-0.16}$. Our results highlight the limitations of methods assuming average, smooth transmission functions. We also present MOSFIRE data for a sample of seven Ly C candidates selected based on photometric redshifts at z > 3.4, but find that all seven have overestimated photometric redshifts by Δz ∼ 0.2 making them unsuitable for Ly C measurements. This results likely due to a bias induced by our selection criteria.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".