HST F160W Imaging of Very Massive Galaxies at 1.5 < z < 3.0: Diversity of Structures and the Effect of Close Pairs on Number Density Estimates
Bibliographic record
Abstract
Abstract We present a targeted follow-up Hubble Space Telescope WFC3 F160W imaging study of very massive galaxies ( ) selected from a combination of ground-based near-infrared galaxy surveys (UltraVISTA, NEWFIRM Medium Band Survey-II , UKIRT Infrared Deep Sky Survey (UKIDSS) Ultra-Deep Survey (UDS) at 1.5 < z < 3). We find that these galaxies are diverse in their structures, with ∼1/3 of the targets being composed of close pairs, and span a wide range in sizes. At 1.5 < z < 2.5, the sizes of both star-forming and quiescent galaxies are consistent with the extrapolation of the stellar mass–size relations determined at lower stellar masses. At 2.5 < z < 3.0, however, we find evidence that quiescent galaxies are systematically larger than expected based on the extrapolation of the relation derived using lower stellar mass galaxies. We used the observed light profiles of the blended systems to decompose their stellar masses and investigate the effect of the close pairs on the measured number densities of very massive galaxies in the early universe. We estimate correction factors to account for close-pair blends and apply them to the observed stellar mass functions (SMFs) measured using ground-based surveys. Given the large uncertainties associated with this extreme population of galaxies, there is currently little tension between the (blending-corrected) number density estimates and predictions from theoretical models. Although we currently lack the statistics to robustly correct for close-pair blends, we show that this is a systematic effect that can reduce the observed number density of very massive galaxies by up to a factor of ∼1.5, and should be accounted for in future studies of SMFs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".