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Record W2039354239 · doi:10.1088/0067-0049/194/2/42

SUPERMASSIVE BLACK HOLE MASS ESTIMATES USING SLOAN DIGITAL SKY SURVEY QUASAR SPECTRA AT 0.7 &lt; <i>z</i> &lt; 2

2011· article· en· W2039354239 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsYork University
Fundersnot available
KeywordsQuasarReverberation mappingSupermassive black holeSkySpectral lineBlack hole (networking)Line (geometry)

Abstract

fetched live from OpenAlex

We present MgII-based black hole mass estimates for 27,602 quasars with rest-frame UV spectra available in the Sloan Digital Sky Survey Data Release Three. This estimation is possible due to the existence of an empirical correlation between the radius of the broad line region and the continuum luminosity at 3000 Angstroms. We regenerate this correlation by applying our measurement method to UV spectra of low-redshift quasars in the HST/IUE databases which have corresponding reverberation mapping estimates of the Hbeta broad line region's radius. Our mass estimation method uses the line dispersion rather than the full width at half maximum of the low-ionization MgII emission line. We measure MgII line dispersions for quasars whose spectra have been reconstructed using the most significant eigenspectra produced through Principal Component Analysis. We have tested the reliability of using reconstructed spectra in black hole mass estimation using a Monte Carlo simulation and by comparing the results from original and reconstructed Data Release Three spectra. We show that using reconstructed spectra not only makes bias-free mass estimation possible for quasars with low spectroscopic signal-to-noise ratio, but also reduces the intrinsic scatter of the distribution of the black hole masses to lower than 0.15 dex.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
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 studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.224
Teacher spread0.206 · 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 teacher head, not a consensus.

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

Citations64
Published2011
Admission routes1
Has abstractyes

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