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Record W2613692480 · doi:10.1093/mnrasl/slx076

Discovery of a dual active galactic nucleus with ∼8 kpc separation

2017· article· en· W2613692480 on OpenAlexaff
Sara L. Ellison, Nathan J. Secrest, J. Trevor Mendel, Shobita Satyapal, Luc Simard

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society Letters · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of AstrophysicsUniversity of Victoria
Fundersnot available
KeywordsPhysicsAstrophysicsActive galactic nucleusGalaxyObservatoryInfraredAstronomy

Abstract

fetched live from OpenAlex

Abstract Targeted searches for dual active galactic nuclei (AGNs), with separations 1–10 kpc, have yielded relatively few successes. A recent pilot survey by Satyapal et al. has demonstrated that mid-infrared (mid-IR) pre-selection has the potential to significantly improve the success rate for dual AGN confirmation in late stage galaxy mergers. In this Letter, we combine mid-IR selection with spatially resolved optical AGN diagnostics from the Mapping Nearby Galaxies at Apache Point Observatory survey to identify a candidate dual AGN in the late stage major galaxy merger SDSS J140737.17+442856.2 at z = 0.143. The nature of the dual AGN is confirmed with Chandra X-ray observations that identify two hard X-ray point sources with intrinsic (corrected for absorption) 2–10 keV luminosities of 4 × 1041 and 3.5 × 1043 erg s−1 separated by 8.3 kpc. The neutral hydrogen absorption (∼1022 cm−2) towards the two AGNs is lower than in duals selected solely on their mid-IR colours, indicating that strategies that combine optical and mid-IR diagnostics may complement techniques that identify the highly obscured dual phase, such as at high X-ray energies or mid-IR only.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.571

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 communication0.0000.001
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 head0.206
Teacher spread0.200 · 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 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

Citations44
Published2017
Admission routes1
Has abstractyes

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