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Record W6920254304 · doi:10.60692/7mqg8-30376

AGN STORM 2. VI. Mapping Temperature Fluctuations in the Accretion Disk of Mrk 817

2024· article· en· W6920254304 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2024
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsYork University
Fundersnot available
KeywordsGalaxyActive galactic nucleusAccretion discFlux (metallurgy)Balmer seriesAccretion (finance)Smoothing

Abstract

fetched live from OpenAlex

Abstract We fit the UV/optical lightcurves of the Seyfert 1 galaxy Mrk 817 to produce maps of the accretion disk temperature fluctuations δ T resolved in time and radius. The δ T maps are dominated by coherent radial structures that move slowly ( v ≪ c ) inward and outward, which conflicts with the idea that disk variability is driven only by reverberation. Instead, these slow-moving temperature fluctuations are likely due to variability intrinsic to the disk. We test how modifying the input lightcurves by smoothing and subtracting them changes the resulting δ T maps and find that most of the temperature fluctuations exist over relatively long timescales (hundreds of days). We show how detrending active galactic nucleus (AGN) lightcurves can be used to separate the flux variations driven by the slow-moving temperature fluctuations from those driven by reverberation. We also simulate contamination of the continuum emission from the disk by continuum emission from the broad-line region (BLR), which is expected to have spectral features localized in wavelength, such as the Balmer break contaminating the U band. We find that a disk with a smooth temperature profile cannot produce a signal localized in wavelength and that any BLR contamination should appear as residuals in our model lightcurves. Given the observed residuals, we estimate that only ∼20% of the variable flux in the U and u lightcurves can be due to BLR contamination. Finally, we discus how these maps not only describe the data but can make predictions about other aspects of AGN variability.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
GPT teacher head0.220
Teacher spread0.201 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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