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Record W4289337225 · doi:10.1093/mnras/stac2140

MIGHTEE: the nature of the radio-loud AGN population

2022· article· en· W4289337225 on OpenAlexfundno aff
I. H. Whittam, M. J. Jarvis, Catherine Hale, M. Prescott, L. K. Morabito, Ian Heywood, Nathan Adams, J. Afonso, Fangxia An, Yiping Ao, R. A. A. Bowler, J. D. Collier, Roger Deane, J. Delhaize, B. S. Frank, Marcin Glowacki, Peter Hatfield, Natasha Maddox, L. Marchetti, A. M. Matthews, I. Prandoni, Solohery M. Randriamampandry, Zara Randriamanakoto, D. J. B. Smith, A. R. Taylor, Nicole Thomas, M. Vaccari

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersInstitut national des sciences de l'UniversFundação para a Ciência e a TecnologiaJapan Society for the Promotion of ScienceAustralian Research CouncilMedical Research CouncilCenter for High Performance ComputingNational Astronomical Observatory of JapanCentre National de la Recherche ScientifiqueCape Peninsula University of TechnologyLiaoning Medical UniversityUniversity of the Western CapeCabinet Office, Government of JapanToray Science FoundationHigh Energy Accelerator Research OrganizationUniversity of TokyoUniversity of CapetownUniversity of PretoriaNational Research FoundationNuclear Fuel Cycle and Supply ChainAcademia SinicaData Storage InstituteDepartment of Science and Innovation, South AfricaUniversity of Cape TownLeverhulme TrustH2020 European Research CouncilHuntington Society of CanadaEuropean School of OncologyCalifornia Earthquake AuthorityScience and Technology Facilities CouncilNeurosciences Research FoundationMinistero degli Affari Esteri e della Cooperazione InternazionaleAustralian GovernmentJapan Science and Technology AgencyNational Natural Science Foundation of ChinaUniversity of Hawai'iMinistry of Education, Culture, Sports, Science and TechnologyPrinceton UniversityU.S. Nuclear Regulatory CommissionNational Research Council Sri LankaFoundation for Ichthyosis and Related Skin TypesCollege of Natural Resources and Sciences, Humboldt State UniversityNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsAstronomyPopulationDemography

Abstract

fetched live from OpenAlex

ABSTRACT We study the nature of the faint radio source population detected in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Early Science data in the COSMOS field, focusing on the properties of the radio-loud active galactic nuclei (AGNs). Using the extensive multiwavelength data available in the field, we are able to classify 88 per cent of the 5223 radio sources in the field with host galaxy identifications as AGNs (35 per cent) or star-forming galaxies (54 per cent). We select a sample of radio-loud AGNs with redshifts out to z ∼ 6 and radio luminosities 1020 < L1.4 GHz/W Hz−1 < 1027 and classify them as high-excitation and low-excitation radio galaxies (HERGs and LERGs). The classification catalogue is released with this work. We find no significant difference in the host galaxy properties of the HERGs and LERGs in our sample. In contrast to previous work, we find that the HERGs and LERGs have very similar Eddington-scaled accretion rates; in particular we identify a population of very slowly accreting AGNs that are formally classified as HERGs at these low radio luminosities, where separating into HERGs and LERGs possibly becomes redundant. We investigate how black hole mass affects jet power, and find that a black hole mass ≳ 107.8 M⊙ is required to power a jet with mechanical power greater than the radiative luminosity of the AGN (Lmech/Lbol > 1). We discuss that both a high black hole mass and black hole spin may be necessary to launch and sustain a dominant radio jet.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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 designSimulation or modeling
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

Citations50
Published2022
Admission routes1
Has abstractyes

Explore more

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