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Record W2909766275 · doi:10.1093/mnras/stz223

Breaking the radio – gamma-ray connection in Arp 220

2019· article· en· W2909766275 on OpenAlexafffund
Tova M. Yoast-Hull, Norman Murray

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Science Foundation
KeywordsPhysicsAstrophysicsCosmic rayRadio galaxyGamma rayFlux (metallurgy)PopulationGalaxyAstronomyMillimeterCOSMIC cancer databaseActive galactic nucleusInfrared

Abstract

fetched live from OpenAlex

Recent analyses of the gamma-ray spectrum from the ultra-luminous infrared galaxy Arp 220 have revealed a discrepancy in the cosmic ray energy injection rates derived from the gamma-rays versus the radio emission. While the observed radio emission is consistent with the star formation rate inferred from infrared observations, a significantly higher cosmic ray population is necessary to accurately model the measured gamma-ray flux. To resolve this discrepancy between the radio and gamma-ray observations, we find that we must increase the cosmic ray energy injection rate and account for an infrared optical depth greater than unity. Raising the energy injection rate naturally raises the total gamma-ray flux but also raises the radio flux unless there is also an increase in the energy loss rate for cosmic ray leptons. A optically thick medium results in an increase in energy losses via inverse Compton for cosmic ray leptons and preserves agreement with submillimeter, millimeter, and infrared wavelength observations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.483

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 science0.0010.000
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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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

Citations17
Published2019
Admission routes2
Has abstractyes

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