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Record W4206774210 · doi:10.1051/0004-6361/202140613

Ionised gas kinematics in MaNGA AGN

2022· article· en· W4206774210 on OpenAlexfundno aff
Alice Deconto Machado, Rogemar A. Riffel, Gabriele S Ilha, Sandro B Rembold, Thaisa Storchi‐Bergmann, Rogério Riffel, J. S. Schimoia, Donald P. Schneider, Dmitry Bizyaev, Shuai Feng, Dominika Wylezalek, L. N. da Costa, Janaina Correa do Nascimento, M. A. G. Maia

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratorySmithsonian Astrophysical ObservatoryUniversity of Colorado BoulderMax-Planck-Institut für AstronomieMax-Planck-Institut für AstrophysikInstituto de Astrofísica de AndalucíaFundação de Amparo à Pesquisa do Estado do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da Ciência, Tecnologia e InovaçãoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversity of OxfordYork UniversityCarnegie Institution for ScienceMinisterio de Ciencia, Innovación y UniversidadesCarnegie Mellon UniversityUniversidad Nacional Autónoma de MéxicoUniversity of WashingtonJohns Hopkins UniversityVanderbilt UniversityYale UniversityNew Mexico State UniversityUniversity of PortsmouthLeibniz-GemeinschaftUniversity of Notre DameFundación Bancaria Caixa d'Estalvis i Pensions de BarcelonaOhio State UniversityCarnegie Institution of WashingtonSmithsonian Institution
KeywordsPhysicsAstrophysicsKinematicsAstronomyClassical mechanics

Abstract

fetched live from OpenAlex

Context. Feedback from active galactic nuclei (AGNs) in general seems to play an important role in the evolution of galaxies, although the impact of AGN winds on their host galaxies is still unknown in the absence of a detailed analysis. Aims. We aim to analyse the kinematics of a sample of 170 AGN host galaxies as compared to those of a matched control sample of non-active galaxies from the MaNGA survey in order to characterise and estimate the extents of the narrow-line region (NLR) and of the kinematically disturbed region (KDR) by the AGN. Methods. We defined the observed NLR radius ( r NLR, o ) as the farthest distance from the nucleus within which both [O III ]/H β and [N II ]/H α ratios fall in the AGN region of the BPT diagram, and the H α equivalent width was required to be larger than 3.0 Å. The extent of the KDR ( r KDR, o ) is defined as the distance from the nucleus within which the AGN host galaxies show a more disturbed gas kinematics than the control galaxies. Results. The AGN [O III ] λ 5007 luminosity ranges from 10 39 to 10 41 erg s −1 , and the kinematics derived from the [O III ] line profiles reveal that, on average, the most luminous AGNs ( L [O III ] > 3.8 × 10 40 erg s −1 ) possess higher residual differences between the gaseous and stellar velocities and velocitie dispersions than their control galaxies in all the radial bins. Spatially resolved NLRs and KDRs were found in 55 and 46 AGN host galaxies, with corrected radii 0.2 < r KDR, c < 2.3 kpc and 0.4 < r NLR, c < 10.1 kpc and a relation between the two given by log r KDR, c = (0.53 ± 0.12) log r NLR, c + (1.07 ± 0.22), respectively. On average, the extension of the KDR corresponds to about 30% of that of the NLR. Assuming that the KDR is due to an AGN outflow, we have estimated ionised gas mass outflow rates that range between 10 −5 and ∼1 M ⊙ yr −1 , and kinetic powers that range from 10 34 to 10 40 erg s −1 . Conclusions. Comparing the power of the AGN ionised outflows with the AGN luminosities, they are always below the 0.05 L AGN model threshold for having an important feedback effect on their respective host galaxies. The mass outflow rates (and power) of our AGN sample correlate with their luminosities, populating the lowest AGN luminosity range of the correlations previously found for more powerful sources.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.193
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 designOther design
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

Citations35
Published2022
Admission routes1
Has abstractyes

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