Spectroscopy from Photometry: A Population of Extreme Emission Line Galaxies at 1.7 ≲ z ≲ 6.7 Selected with JWST Medium Band Filters
Bibliographic record
Abstract
Abstract We use JWST/NIRCam medium band photometry in a single pointing of the Canadian NIRISS Unbiased Cluster Survey to identify 118 extreme emission-line galaxies (EELGs) over 1.7 ≲ z ≲ 6.7, selected using a set of color cuts that target galaxies with extreme [O iii ] + H β and H α emission. We show that our medium band color selections are able to select galaxies based on emission-line EW, which is advantageous to more commonly used selections since it does not require strong continuum emission, and can select galaxies with faint or red continuum fluxes. The median EWs of our sample is EW(H α ) = 893 Å and EW ([O III ] + H β ) = 1255 Å and includes some objects with EW ([O III ] + H β ) ∼ 3000 Å. These systems are mostly compact with low stellar mass (median log ( M ⋆ / M ⊙ ) = 8.03 ), low metallicity (median Z = 0.14 Z ⊙ ), little dust (median A V = 0.18 mag), and high SSFR (median SSFR = 1.18 × 10 −8 yr −1 ). Additionally, galaxies in our sample show increasing EW(H α ) and EW([O iii ] + H β ) with redshift, an anticorrelation of EW(H α ) with stellar mass, and no correlation between EW([O iii ] + H β ) and stellar mass. Finally, we present NIRSpec spectroscopy of 15 of the EELGs in our sample. These spectra confirm the redshifts and EWs of the EELGs calculated from the medium bands, which demonstrates the accuracy and efficiency of our color selections. Overall, we show that there are significant advantages to using medium band photometry to identify and study EELGs at a wide range of redshifts.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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".