Constraints on the Size Evolution of Brightest Cluster Galaxies
Bibliographic record
Abstract
We measure the luminosity profiles of 16 brightest cluster galaxies (BCGs) at 0.4 < z < 0.8 using high-resolution F160W NICMOS and F814W WFPC2 Hubble Space Telescope imaging. The heterogeneous sample is drawn from a variety of surveys: seven from clusters in the Einstein Medium Sensitivity Survey, five from the Las Campanas Distant Cluster Survey and its Northern Hemisphere precursor, and the remaining four from traditional optical surveys. We find that the surface brightness profiles of all but three of these BCGs are well described by a standard de Vaucouleurs ( r 1/4 ) profile out to at least ~2 r e and that the biweight-estimated NICMOS effective radius of our high-redshift BCGs ( r e = 8.3 ± 1.4 kpc for H 0 = 80 km s -1 Mpc -1 , Ω m = 0.2, Ω Λ = 0.0) is ~2 times smaller than that measured for a local BCG sample. If high-redshift BCGs are in dynamical equilibrium and satisfy the same scaling relations as low-redshift ones, this change in size would correspond to a mass growth of a factor of 2 since z ~ 0.5. However, the biweight-estimated WFPC2 effective radius of our sample is 18 ± 5.1 kpc, which is fully consistent with the local sample. While we can rule out mass accretion rates higher than a factor of 2 in our sample, the discrepancy between our NICMOS and WFPC2 results, which after various tests we describe appears to be physical, does not yet allow us to place strong constraints on accretion rates below that level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".