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Record W2944565007 · doi:10.1093/mnras/stz3244

Understanding extreme quasar optical variability with CRTS – II. Changing-state quasars

2019· article· en· W2944565007 on OpenAlexfundno aff
M. J. Graham, Nicholas P. Ross, Daniel Stern, A. J. Drake, Barry McKernan, K. E. Saavik Ford, S. G. Djorgovski, A. Mahabal, Eilat Glikman, Steve Larson, E. Christensen

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryYork UniversityScience and Technology Facilities CouncilCarnegie Mellon UniversityOffice of ScienceJohns Hopkins UniversityCollege of Engineering, Michigan State UniversityHarvard UniversityOhio State UniversityNew Mexico State UniversityUniversity of PortsmouthYale UniversityVanderbilt UniversityNational Science FoundationUniversity of WashingtonAlfred P. Sloan FoundationBrookhaven National LaboratoryU.S. Department of EnergyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNational Science Foundation of Sri LankaJet Propulsion LaboratoryPrinceton University
KeywordsQuasarPhysicsCRTSAstrophysicsAstronomyOVV quasarGalaxy

Abstract

fetched live from OpenAlex

ABSTRACT We present the results of a systematic search for quasars in the Catalina Real-time Transient Survey exhibiting both strong photometric variability and spectroscopic variability over a decadal baseline. We identify 111 sources with specific patterns of optical and mid-infrared photometric behaviour and a defined spectroscopic change. These ‘changing-state’ quasars (CSQs) form a higher luminosity sample to complement existing sets of ‘changing-look’ AGNs and quasars in the literature. The CSQs (by selection) exhibit larger photometric variability than the changing-look quasars (CLQs). The spectroscopic variability is marginally stronger in the CSQs than CLQs as defined by the change in H β/[$\rm {O \,\rm {\small {III}}}$] ratio. We find 48 sources with declining H β flux and 63 sources with increasing H β flux, and discover 8 sources with $z$ > 0.8, further extending the redshift arm. Our CSQ sample compares to the literature CLQ objects in similar distributions of H β flux ratios and differential Eddington ratios between high (bright) and low (dim) states. Taken as a whole, we find that this population of extreme varying quasars is associated with changes in the Eddington ratio and the time-scales imply cooling/heating fronts propagating through the disc.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.105
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.018
GPT teacher head0.196
Teacher spread0.179 · 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.

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

Citations117
Published2019
Admission routes1
Has abstractyes

Explore more

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