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Radio Galaxies at z = 1.1 to 3.8: Adaptive-Optics Imaging and Archival Hubble Space Telescope Data

2001· preprint· en· W6910535708 on OpenAlexaffabout

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2001
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsHubble Deep Field SouthHubble space telescopeGalaxyRedshiftHubble Ultra-Deep FieldHubble Deep FieldRadio galaxyQuasarUltraviolet astronomy

Abstract

fetched live from OpenAlex

We have undertaken a program of high-resolution imaging of high-redshift radio galaxies (HzRGs) using adaptive optics on the Canada-France-Hawaii Telescope. We report on deep imaging in J, H,and K bands of 6 HzRGs in the redshift range 1.1 to 3.8. At these redshifts, near-infrared bandpasses sample the rest-frame visible galaxian light. The radio galaxy is resolved in all the fields and is generally elongated along the axis of the radio lobes. These images are compared to archival Hubble Space Telescope Wide-Field Planetary Camera 2 optical observations of the same fields and show the HzRG morphology in rest-frame ultraviolet and visible light is generally very similar: a string of bright compact knots. Furthermore, this sample - although very small - suggests the colors of the knots are consistent with light from young stellar populations. If true, a plausible explanation is that these objects are being assembled by mergers at high redshift.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.029
GPT teacher head0.259
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2001
Admission routes2
Has abstractyes

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