SPECTROSCOPY OF LUMINOUS<i>z</i>> 7 GALAXY CANDIDATES AND SOURCES OF CONTAMINATION IN<i>z</i>> 7 GALAXY SEARCHES
Bibliographic record
Abstract
We present three bright z + -dropout candidates selected from deep near-infrared (NIR) imaging of the COSMOS 2 deg 2 field. All three objects match the 0.8–8 μm colors of other published z > 7 candidates but are 3 mag brighter, facilitating further study. Deep spectroscopy of two of the candidates covering 0.64–1.02 μm with Keck-DEIMOS and all three covering 0.94–1.10 μm and 1.52–1.80 μm with Keck-NIRSPEC detects weak spectral features tentatively identified as Lyα at z = 6.95 and z = 7.69 in two of the objects. The third object is placed at z ∼ 1.6 based on a 24 μm and weak optical detection. A comparison with the spectral energy distributions of known z < 7 galaxies, including objects with strong spectral lines, large extinction, and large systematic uncertainties in the photometry, yields no objects with similar colors. However, the λ > 1 μm properties of all three objects can be matched to optically detected sources with photometric redshifts at z ∼ 1.8, so the non-detection in the i + and z + bands is the primary factor which favors a z > 7 solution. If any of these objects are at z ∼ 7, the bright end of the luminosity function is significantly higher at z > 7 than suggested by previous studies, but consistent within the statistical uncertainty and the dark matter halo distribution. If these objects are at low redshift, the Lyman break selection must be contaminated by a previously unknown population of low-redshift objects with very strong breaks in their broadband spectral energy distributions and blue NIR colors. The implications of this result on luminosity function evolution at high redshift are discussed. We show that the primary limitation of z > 7 galaxy searches with broad filters is the depth of the available optical data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".