Exposing Line Emission: The Systematic Differences of Measuring Galaxy Stellar Masses with JWST NIRCam Medium versus Wide Band Photometry
Bibliographic record
Abstract
Abstract Photometrically derived stellar masses are known to suffer from systematic uncertainties, particularly due to nebular emission contributions to the spectral energy distribution (SED). Using James Webb Space Telescope NIRCam imaging from the CAnadian NIRISS Unbiased Cluster Survey, we introduce a comparison study of photometrically derived redshifts and stellar masses based on two photometric catalogs of the same field spanning ∼0.4–4.5 μ m: one consisting solely of wide band photometry, and another employing a combination of wide and medium band photometry. We find that ∼70% of likely line emitters have consistent photometric redshifts between both catalogs, with a median stellar mass difference between the two catalogs of <0.2 dex across all redshift bins. There are, however, a subset of galaxies (5% at z ∼ 2 up to 15% at z ∼ 6) where wide bands underestimate emission line strengths and infer older stellar populations, leading to median stellar mass differences of ∼0.7 dex. Examination of the SEDs for galaxies with inconsistent photometric redshifts shows this is caused by the inability of the wide bands to distinguish continuum emission from emission lines. Computing a stellar mass density with our sample, we find that it is potentially underestimated using wide band photometry by ∼10%–20% at z < 4, and potentially overestimated by as much as a factor of 2–3 at z > 5. These systematic differences caused by the poor spectral resolution of wide bands have implications for both ongoing and future planned observing programs that determine stellar mass and other physical properties of high-redshift galaxies solely via wide band photometry.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".