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Record W4221152292 · doi:10.3847/1538-4357/ac5682

Investigating the Low-flux States in Six Intermediate Polars

2022· article· en· W4221152292 on OpenAlexaff
Ava E. Covington, A. W. Shaw, K. Mukai, Colin Littlefield, C. O. Heinke, Richard M. Plotkin, David Barrett, David A. Boyd, Stephen M. Brincat, Rolf Carstens, Donald F. Collins, Lewis M. Cook, Walter R. Cooney, Sjoerd Dufoer, Shawn Dvorak, Charles Galdies, William D. Goff, F.-J. Hambsch, Steve Johnston, Kenneth Menzies, L. A. G. Monard, Etienne Morelle, Peter S. Nelson, Y. Öǧmen, John Rock, Richard Sabo, Jim Seargeant, Joseph Ulowetz, Tonny Vanmunster

Bibliographic record

VenueThe Astrophysical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsFlux (metallurgy)Accretion (finance)AstrophysicsAccretion discPhotometry (optics)Intermediate polarMaterials scienceStarsWhite dwarf

Abstract

fetched live from OpenAlex

Abstract We present optical photometry of six intermediate polars that exhibit transitions to a low-flux state. For four of these systems, DW Cnc, V515 And, V1223 Sgr, and RX J2133.7+5107, we are able to perform timing analysis in and out of the low states. We find that, for DW Cnc and V515 And, the dominant periodicities in the light curves change as the flux decreases, indicating a change in the sources’ accretion properties as they transition to the low state. For V1223 Sgr, we find that the variability is almost completely quenched at the lowest flux, but we do not find evidence for a changing accretion geometry. For RX J2133.7+5107, the temporal properties do not change in the low state, but we do see a period of enhanced accretion that is coincident with increased variability on the beat frequency, which we do not associate with a change in the accretion mechanisms in the system.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.011
GPT teacher head0.231
Teacher spread0.219 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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