DO SUBMILLIMETER GALAXIES REALLY TRACE THE MOST MASSIVE DARK-MATTER HALOS? DISCOVERY OF A HIGH-z CLUSTER IN A HIGHLY ACTIVE PHASE OF EVOLUTION
Bibliographic record
Abstract
We present detailed observations of a z ~ 1.99 cluster of submillimeter galaxies (SMGs), discovered as the strongest redshift spike in our entire survey of ~100 SMGs across 800 arcmin2. It is the largest blank-field SMG concentration currently known and has <0.01% chance of being drawn from the underlying selection function for SMGs. We have compared UV observations of galaxies at this redshift, where we find a much less dramatic overdensity, having an 11% chance of being drawn from its selection function. We use this z ~ 1.99 overdensity to compare the biasing of UV- and submillimeter-selected galaxies, and test whether SMGs could reside in less overdense environments, with their apparent clustering signal being dominated by highly active merger periods in modest mass structures. We discuss the probable mechanisms for the apparently different bias we see at the two wavelengths. This impressively active formation phase in a low-mass cluster is not something seen in simulations, although we propose a toy model using merger bias, which could account for the bias seen in the SMGs. While enhanced buildup of stellar mass appears characteristic of other high-z galaxy clusters, neither the UV galaxies nor SMGs in this structure exhibit larger stellar masses than their field galaxy counterparts (although the excess of SMGs in the structure represents a larger volume-averaged stellar mass than the field). Our findings have strong implications for future surveys of high-z galaxies at long wavelengths such as SCUBA2 and Herschel. We suggest that since these surveys will select galaxies during their episodes of peak starbursts, they could probe a much wider range of environments than just the progenitors of rich clusters, revealing more completely the key events and stages in galaxy formation and assembly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".