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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".