MAGAZ3NE: Massive, Extremely Dusty Galaxies at z ∼ 2 Lead to Photometric Overestimation of Number Densities of the Most Massive Galaxies at 3 < z < 4*
Bibliographic record
Abstract
Abstract We present rest-frame optical spectra from Keck/MOSFIRE and Keck/NIRES of 16 candidate ultramassive galaxies targeted as part of the Massive Ancient Galaxies at z > 3 Near-Infrared Survey (MAGAZ3NE). These candidates were selected to have photometric redshifts 3 ≲ z phot <4, photometric stellar masses log ( M ∗ / M ⊙ ) > 11.7, and well-sampled photometric spectral energy distributions (SEDs) from the UltraVISTA and VIDEO surveys. In contrast to previous spectroscopic observations of blue star-forming and poststarburst ultramassive galaxies, candidates in this sample have very red SEDs implying significant dust attenuation, old stellar ages, and/or active galactic nuclei (AGN). Of these galaxies, eight are revealed to be heavily dust-obscured 2.0 < z < 2.7 galaxies with strong emission lines, some showing broad features indicative of AGN, three are Type I AGN hosts at z > 3, one is a z ∼ 1.2 dusty galaxy, and four galaxies do not have a confirmed spectroscopic redshift. In fact, none of the sample has ∣ z spec − z phot ∣ < 0.5, suggesting difficulties for photometric redshift programs in fitting similarly red SEDs. The prevalence of these red interloper galaxies suggests that the number densities of high-mass galaxies are overestimated at z ≳ 3 in large photometric surveys, helping to resolve the “impossibly early galaxy problem” and leading to much better agreement with cosmological galaxy simulations. A more complete spectroscopic survey of ultramassive galaxies is required to pin down the uncertainties on their number densities in the early Universe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".