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Record W3198663794 · doi:10.1093/mnras/stab3217

The variability of brightest cluster galaxies at high radio frequencies

2021· preprint· en· W3198663794 on OpenAlexafffund
Tom Rose, A. C. Edge, S. Kiehlmann, Junhyun Baek, Aeree Chung, Taehyun Jung, Jae-Woo Kim, A. C. S. Readhead, Aidan Sedgewick

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2021
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of Waterloo
FundersH2020 European Research CouncilNational Institutes of Natural SciencesCanadian Space AgencyNational Research Foundation of KoreaCalifornia Institute of TechnologyEuropean CommissionAcademia SinicaKorea Institute of Science and TechnologyNational Astronomical Observatory of JapanAalto-YliopistoMinistry of Science and ICT, South KoreaNational Science CouncilScience and Technology Facilities CouncilNational Research FoundationKorea Institute of Science and Technology InformationKorea Research Environment Open NetworkNational Aeronautics and Space AdministrationKorea Astronomy and Space Science InstituteNational Science Foundation
KeywordsAstrophysicsVery-long-baseline interferometryPhysicsTelescopeRadio telescopeObservatoryAmplitudeGalaxyAstronomyBlazarCluster (spacecraft)Gamma ray

Abstract

fetched live from OpenAlex

ABSTRACT Variability of a galaxy’s core radio source can be a significant consequence of active galactic nucleus accretion. However, this variability has not been well studied, particularly at high radio frequencies. As such, we report on a campaign monitoring the high radio frequency variability of 20 nearby, cool-core brightest cluster galaxies. From our representative sample, we show that most vary significantly on time-scales of approximately 1 yr and longer. Our highest cadence observations are at 15 GHz and are from the Owens Valley Radio Observatory. They have a median time interval of 7 d and mostly span between 8 and 13 yr. We apply a range of variability detection techniques to the sources’ light curves to analyse changes on week to decade long time-scales. Most notably, at least half of the sources show 20 per cent peak to trough variability on 3 yr time-scales, while at least a third vary by 60 per cent on 6 yr time-scales. Significant variability, which is important to studies of the Sunyaev–Zel’dovich Effect in the radio/sub-mm, is therefore a common feature of these sources. We also show how the variability relates to spectral properties at frequencies of up to 353 GHz using data from the Korean VLBI network, the NIKA2 instrument of the IRAM 30-m telescope, and the SCUBA-2 instrument of the James Clerk Maxwell Telescope.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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 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
Published2021
Admission routes2
Has abstractyes

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