Introducing the FLAMINGOS-2 Split-K Medium-band Filters: The Impact on Photometric Selection of High-z Galaxies in the FENIKS-pilot survey
Bibliographic record
Abstract
Abstract Deep near-infrared photometric surveys are efficient in identifying high-redshift galaxies, however, they can be prone to systematic errors in photometric redshift. This is particularly salient when there is limited sampling of key spectral features of a galaxy’s spectral energy distribution (SED), such as for quiescent galaxies where the expected age-sensitive Balmer/4000 Å break enters the K-band at z > 4. With single-filter sampling of this spectral feature, degeneracies between SED models and redshift emerge. A potential solution to this comes from splitting the K band into multiple filters. We use simulations to show an optimal solution is to add two medium-band filters, K blue (λ cen = 2.06 μm, Δλ = 0.25 μm) and K red (λ cen = 2.31 μm, Δλ = 0.27 μm), that are complementary to the existing K s filter. We test the impact of the K-band filters with simulated catalogs comprised of galaxies with varying ages and signal-to-noise. The results suggest that the K-band filters do improve photometric redshift constraints on z > 4 quiescent galaxies, increasing precision and reducing outliers by up to 90%. We find that the impact from the K-band filters depends on the signal-to-noise, the redshift, and the SED of the galaxy. The filters we designed were built and used to conduct a pilot of the FLAMINGOS-2 Extragalactic Near-Infrared K-band Split survey. While no new z > 4 quiescent galaxies are identified in the limited area pilot, the K blue and K red filters indicate strong Balmer/4000 Å breaks in existing candidates. Additionally, we identify galaxies with strong nebular emission lines, for which the K-band filters increase photometric redshift precision and in some cases indicate extreme star formation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".