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Record W2912309921 · doi:10.1093/mnras/stz260

Nuclear spallation in active galaxies

2019· article· en· W2912309921 on OpenAlexaff
Luigi Gallo, J.S. Randhawa, Sophia G. H. Waddell, Maan H Hani, Javier A. García, C. S. Reynolds

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of VictoriaSaint Mary's University
FundersScience and Technology Facilities Council
KeywordsSpallationPhysicsAstrophysicsSpectral lineAccretion (finance)IonizationNucleosynthesisGalaxyNuclear physicsAstronomyNeutronStars

Abstract

fetched live from OpenAlex

A number of works point to the presence of narrow emission features at unusual energies in the X-ray spectra of active galactic nuclei or to potentially low iron abundances as possible evidence for the spallation of iron. With the imminence of high-resolution calorimeter spectroscopy, the potential to test spallation models in astrophysical sources will soon be possible. Previously determined nuclear spallation reactions of Fe are recalculated making use of improved total inelastic and partial reaction cross-sections that result in different absolute and relative abundances of the main spallation elements Mn, Cr, V, and Ti. The effects of ionization and dynamics near the black hole are examined in simulated spectra with CCD and calorimeter (i.e. Hitomi) resolution. With high resolution, differences in relative abundances and ionization should be detectable if spallation is originating at large distances from the black hole (e.g. torus or disc wind), where blurring is not significant. If spallation were occurring in the inner accretion disc, it would likely be undetected as blurring effects would cause significant blending of spectral features.

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)0.001
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.007
GPT teacher head0.194
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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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