The impact of medium-width bands on the selection and subsequent luminosity function measurements of high-<i>z</i> galaxies
Bibliographic record
Abstract
ABSTRACT New, ultra-deep medium-width photometric coverage with James Webb Space Telescope (JWST)’s NIRCam instrument provides the potential for much improved photo-z reliability at high redshifts. In this study, we conduct a systematic analysis of the JADES Origins Field, which contains 14 broad- and medium-width near-infrared bands, to assess the benefits of medium band photometry on high-z completeness and contamination rates. Using imaging reaching AB mag $29.8\!-\!30.35$ depth, we test how high-z selections differ when images are artificially degraded or bands are removed. In parallel, the same experiments are conducted on simulated catalogues from the JAGUAR semi-analytic model to examine if observations can be replicated. We find sample completeness is high ($80~{{\rm per\,cent}}+$) and contamination low ($\lt 4~{{\rm per\,cent}}$) when in the $10\sigma +$ regime, even without the use of any medium-width bands. The addition of medium-width bands leads to increases in completeness ($\sim 10~{{\rm per\,cent}}$), but multiple bands are required to improve contamination rates due to the small redshift ranges over which they probe strong emission lines. Incidents of Balmer–Lyman degeneracy increase in the $5{\!-\!}7\sigma$ regime and this can be replicated in both simulated catalogues and degraded real data. We measure the faint-end of the ultraviolet luminosity function (UV LF) at $8.5\lt z\lt 13.5$, finding high number densities that agree with previous JWST observations. Overall, medium bands are effective at increasing completeness and reducing contamination, but investment in achieving comparable depths in the blue ($\lt 1.5\,\mu$m) as achieved in the red is also found to be key to fully reducing contamination from high-z samples.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".