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Record W1988991313 · doi:10.1088/0004-637x/706/1/184

MILLIMETER OBSERVATIONS OF A SAMPLE OF HIGH-REDSHIFT OBSCURED QUASARS

2009· article· en· W1988991313 on OpenAlexaff
Alejo Martínez‐Sansigre, A. Karim, Eva Schinnerer, A. Omont, D. J. B. Smith, Jingwen Wu, Gary J. Hill, H.-R. Klöckner, Mark Lacy, Steve Rawlings, Chris J. Willott

Bibliographic record

VenueThe Astrophysical Journal · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsNational Research Council CanadaHerzberg Institute of Astrophysics
FundersScience and Technology Facilities CouncilCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsRedshiftQuasarLuminosityPlateau de Bure InterferometerActive galactic nucleusExtinction (optical mineralogy)Star formationInfraredAstronomySpectral energy distributionCosmic infrared backgroundGalaxyCosmic microwave background

Abstract

fetched live from OpenAlex

We present observations at 1.2 mm with Max-Planck Millimetre Bolometer Array (MAMBO-II) of a sample of z ≳ 2 radio-intermediate obscured quasars, as well as CO observations of two sources with the Plateau de Bure Interferometer. The typical rms noise achieved by the MAMBO observations is 0.55 mJy beam −1 and five out of 21 sources (24%) are detected at a significance of ⩾3σ. Stacking all sources leads to a statistical detection of 〈 S 1.2 mm 〉 = 0.96 ± 0.11 mJy and stacking only the non-detections also yields a statistical detection, with 〈 S 1.2 mm 〉 = 0.51 ± 0.13 mJy. At the typical redshift of the sample, z = 2, 1 mJy corresponds to a far-infrared luminosity L FIR ∼4 × 10 12 L ☉ . If the far-infrared luminosity is powered entirely by star formation, and not by active galactic nucleus heated dust, then the characteristic inferred star formation rate is ∼700 M ☉ yr −1 . This far-infrared luminosity implies a dust mass of M d ∼3 × 10 8 M ☉ , which is expected to be distributed on ∼kpc scales. We estimate that such large dust masses on kpc scales can plausibly cause the obscuration of the quasars. Combining our observations at 1.2 mm with mid- and far-infrared data, and additional observations for two objects at 350 μm using SHARC-II, we present dust spectral energy distributions (SEDs) for our sample and derive a mean SED for our sample. This mean SED is not well fitted by clumpy torus models, unless additional extinction and far-infrared re-emission due to cool dust are included. This additional extinction can be consistently achieved by the mass of cool dust responsible for the far-infrared emission, provided the bulk of the dust is within a radius ∼2–3 kpc. Comparison of our sample to other samples of z ∼ 2 quasars suggests that obscured quasars have, on average, higher far-infrared luminosities than unobscured quasars. There is a hint that the host galaxies of obscured quasars must have higher cool-dust masses and are therefore often found at an earlier evolutionary phase than those of unobscured quasars. For one source at z = 2.767, we detect the CO(3–2) transition, with S CO Δν = 630 ± 50 mJy km s −1 , corresponding to L CO(3-2) = 3.2 × 10 7 L ☉ , or a brightness-temperature luminosity of L ' CO(3-2) = 2.4 × 10 10 K km s −1 pc 2 . For another source at z = 4.17, the lack of detection of the CO(4–3) line suggests the line to have a brightness-temperature luminosity L ' CO(4-3) < 1 × 10 10 K km s −1 pc 2 . Under the assumption that in these objects the high- J transitions are thermalized, we can estimate the molecular gas contents to be M ☉ and <8 × 10 9 M ☉ , respectively. The estimated gas depletion timescales are τ g = 4 Myr and <16 Myr, and low gas-to-dust mass ratios of M g / M d = 19 and <20 are inferred. These values are at the low end but consistent with those of other high-redshift galaxies.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.225
Teacher spread0.209 · 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 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

Citations41
Published2009
Admission routes1
Has abstractyes

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