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Record W2891947279 · doi:10.3847/1538-4357/ab03d9

Gemini GNIRS Near-infrared Spectroscopy of 50 Quasars at z ≳ 5.7

2019· article· en· W2891947279 on OpenAlexaff
Yue Shen, Linhua Jiang, Eduardo Bañados, Xiaohui Fan, Luis C. Ho, Dominik A. Riechers, Michael A. Strauss, Bram Venemans, M. Vestergaard, Fabian Walter, Feige Wang, Chris J. Willott, Xue-Bing Wu, Jinyi Yang

Bibliographic record

VenueThe Astrophysical Journal · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsPhysicsQuasarInfraredSpectroscopyAstronomyAstrophysicsInfrared spectroscopyGalaxy

Abstract

fetched live from OpenAlex

Abstract We report initial results from a large Gemini program to observe z ≳ 5.7 quasars with GNIRS near-IR spectroscopy. Our sample includes 50 quasars with simultaneous ∼0.85–2.5 μ m spectra covering the rest-frame ultraviolet and major broad emission lines from Lyα to Mg ii . We present spectral measurements for these quasars and compare with their lower redshift counterparts at z = 1.5–2.3. We find that when quasar luminosity is matched, there are no significant differences between the rest-UV spectra of z ≳ 5.7 quasars and the low- z comparison sample. High- z quasars have similar continuum and emission line properties and occupy the same region in the black hole mass and luminosity space as the comparison sample, accreting at an average Eddington ratio of ∼0.3. There is no evidence for super-Eddington accretion or hypermassive (>10 10 M ⊙ ) black holes within our sample. We find a mild excess of quasars with weak C iv lines relative to the control sample. Our results, corroborating earlier studies but with better statistics, demonstrate that these high- z quasars are already mature systems of accreting supermassive black holes operating with the same physical mechanisms as those at lower redshifts.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.006
GPT teacher head0.214
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations189
Published2019
Admission routes1
Has abstractyes

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