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Record W4396921154 · doi:10.3847/1538-4365/ad3936

The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results

2024· article· en· W4396921154 on OpenAlexafffund
Yue Shen, C. J. Grier, K. Horne, Zachary Stone, Jennifer I-Hsiu Li, Qian Yang, Y. Homayouni, Jonathan R. Trump, Scott F. Anderson, W. N. Brandt, Patrick B. Hall, Luis C. Ho, Linhua Jiang, Patrick Petitjean, Donald P. Schneider, C. Tao, Fergus. R. Donnan, Yusra AlSayyad, Matthew A. Bershady, Michael R. Blanton, Dmitry Bizyaev, Kevin Bundy, Yuguang Chen, Megan C. Davis, Kyle Dawson, Xiaohui Fan, Jenny E. Greene, H. Gröller, Yucheng Guo, Héctor J. Ibarra-Medel, Yuanzhe Jiang, Ryan P. Keenan, Juna A. Kollmeier, Cassandra Lejoly, Zefeng Li, Axel de la Macorra, Maxwell Moe, Jundan Nie, Graziano Rossi, Paul S. Smith, Wei Leong Tee, Anne-Marie Weijmans, Jiachuan Xu, Minghao Yue, Xu Zhou, Zhimin Zhou, Hu Zou

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoYork University
FundersSmithsonian Astrophysical ObservatoryUniversity of Colorado BoulderInstituto de Astrofísica de CanariasOffice of ScienceMax-Planck-Institut für AstronomieMax-Planck-Institut für AstrophysikUniversidad Nacional Autónoma de MéxicoNational Central UniversityCentre National de la Recherche ScientifiqueBrookhaven National LaboratoryTsinghua UniversityMinistério da Ciência, Tecnologia e InovaçãoU.S. Department of EnergySmithsonian InstitutionNational Natural Science Foundation of ChinaQueen's University BelfastPennsylvania State UniversityUniversity of OxfordDurham UniversityYork UniversityLeibniz-GemeinschaftUniversity of Notre DameCarnegie Mellon UniversityCollege of Engineering, Michigan State UniversityPrinceton UniversityUniversity of WashingtonAlfred P. Sloan FoundationJohns Hopkins UniversityQueen's UniversityHarvard UniversityOhio State UniversityCarnegie Institution of WashingtonNational Science FoundationNew Mexico State UniversityUniversity of PortsmouthInstitut national des sciences de l'UniversVanderbilt UniversityYale UniversityLawrence Berkeley National LaboratoryUniversity of Pennsylvania
KeywordsRedshiftPhysicsAstrophysicsLuminosityReverberation mappingQuasarSkySpectral lineAstronomyGalaxy

Abstract

fetched live from OpenAlex

Abstract We present the final data from the Sloan Digital Sky Survey (SDSS) Reverberation Mapping (RM) project, a precursor to the SDSS-V Black Hole Mapper RM program. This data set includes 11 yr photometric and 7 yr spectroscopic light curves for 849 broad-line quasars over a redshift range of 0.1 &lt; z &lt; 4.5 and a luminosity range of L bol = 10 44−47.5 erg s −1 , along with spectral and variability measurements. We report 23, 81, 125, and 110 RM lags (relative to optical continuum variability) for broad H α , H β , Mg ii , and C iv using the SDSS-RM sample, spanning much of the luminosity and redshift ranges of the sample. Using 30 low-redshift RM active galactic nuclei with dynamical-modeling black hole masses, we derive a new estimate of the average virial factor of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close="〉" open="〈"> <mml:mrow> <mml:mi>log</mml:mi> <mml:mi>f</mml:mi> </mml:mrow> </mml:mfenced> <mml:mo>=</mml:mo> <mml:mn>0.62</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.07</mml:mn> </mml:math> for the line dispersion measured from the rms spectrum. The intrinsic scatter of individual virial factors is 0.31 ± 0.07 dex, indicating a factor of 2 systematic uncertainty in RM black hole masses. Our lag measurements reveal significant R – L relations for H β and Mg ii at high redshift, consistent with the latest measurements based on heterogeneous samples. While we are unable to robustly constrain the slope of the R – L relation for C iv given the limited dynamic range in luminosity, we found substantially larger scatter in C iv lags at fixed L 1350 . Using the SDSS-RM lag sample, we derive improved single-epoch (SE) mass recipes for H β , Mg ii , and C iv , which are consistent with their respective RM masses as well as between the SE recipes from two different lines, over the luminosity range probed by our sample. The new H β and Mg ii recipes are approximately unbiased estimators at given RM masses, but there are systematic biases in the C iv recipe. The intrinsic scatter of SE masses around RM masses is ∼0.45 dex for H β and Mg ii , increasing to ∼0.58 dex for C iv .

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.598
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 communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.259
Teacher spread0.236 · 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 designTheoretical or conceptual
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

Citations87
Published2024
Admission routes2
Has abstractyes

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