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Record W4387353993 · doi:10.1093/mnras/stad2994

Virial black hole mass estimates of quasars in the XQ-100 legacy survey

2023· article· en· W4387353993 on OpenAlexfundno aff
Samuel Lai, Christopher A. Onken, Christian Wolf, Fuyan Bian, G. Cupani, Sebastián López, V. D’Odorico

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersAustralian Astronomical Optics-MacquarieAustralian Research CouncilQueen's UniversityCurtin University of TechnologyPlanetary Science DivisionScience Mission DirectorateSwinburne University of TechnologyNational Central UniversityMonash UniversityAustralian GovernmentSpace Telescope Science InstituteNational Computational InfrastructureNational Cancer InstituteAustralian National Data ServiceJohns Hopkins UniversityNational Aeronautics and Space AdministrationAustralian National UniversityQueen's University BelfastSmithsonian Astrophysical ObservatoryMax-Planck-Institut für AstronomieAstronomy Australia LimitedSmithsonian InstitutionNational Science Foundation
KeywordsPhysicsQuasarAstrophysicsVirial theoremRedshiftVirial massSpectral lineLuminosityBlack hole (networking)Emission spectrumAstronomyGalaxy

Abstract

fetched live from OpenAlex

ABSTRACT The black hole (BH) mass and luminosity are key factors in determining how a quasar interacts with its environment. In this study, we utilize data from the European Southern Observatory Large Programme XQ-100, a high-quality sample of 100 X-shooter spectra of the most luminous quasars in the redshift range 3.5 < z < 4.5, and measure the properties of three prominent optical and ultraviolet broad emission lines present in the wide wavelength coverage of X-shooter: C iv, Mg ii, and H β. The line properties of all three broad lines are used for virial estimates of the BH mass and their resulting mass estimates for this sample are tightly correlated. The BH mass range is $\log {(\rm {\mathit{ M}_{BH}}/\rm {M_\odot })} = 8.6{\!-\!}10.3$ with bolometric luminosities estimated from the 3000 Å continuum in the range $\log {(\rm {\mathit{ L}_{bol}}/\rm {erg\, s^{-1}})} = 46.7{\!-\!}48.0$. Robustly determined properties of these quasars enable a variety of follow-up research in quasar astrophysics, from chemical abundance and evolution in the broad-line region to radiatively driven quasar outflows.

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)0.002
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical Society→Same topicGalaxies: Formation, Evolution, Phenomena→French-language works237,207→