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Record W4413036142 · doi:10.3847/1538-4357/adeb73

Sleeping Giants Arise: Monitoring the Return of Three Changing-look Quasars to Their High States<sup>*</sup>

2025· article· en· W4413036142 on OpenAlexaff
Laura M. Duffy, Michael Eracleous, John J. Ruan, Qian Yang, Jessie C. Runnoe

Bibliographic record

VenueThe Astrophysical Journal · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsBishop's University
Fundersnot available
KeywordsPhysicsQuasarAstronomyAstrophysicsGalaxy

Abstract

fetched live from OpenAlex

Abstract Changing-look quasars (CLQs) challenge many models of the quasar central engine. Their extreme variability in both the continuum and broad emission-line fluxes on timescales on the order of years is difficult to explain. To investigate the cause of the observed transitions, we present new contemporaneous optical and X-ray observations of three faded CLQs as they return to a state of high optical luminosity. Two of these three remained in a quiescent state for more than 10 yr before returning to a new high state. We find that before, during, and after transition, the spectral energy distributions of all three follow predictions for quasars based on X-ray binary outbursts, suggesting that the underlying mechanism is likely a changing accretion rate causing changes in the accretion flow structure. In two of the three cases, the transition between the initial high and low state and the transition between the low and new high state took nearly identical amounts of time, on the order of hundreds of days. This transition timescale is a useful constraint on models of the accretion state changes. The behavior of the broad emission-line profiles suggests that the broad-line region structure is changing during the transition.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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