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Record W2590222241 · doi:10.3847/1538-4357/aa9375

Bow Ties in the Sky. II. Searching for Gamma-Ray Halos in the Fermi Sky Using Anisotropy

2017· article· en· W2590222241 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsIntergalactic travelHaloActive galactic nucleusReionizationPopulationFermi Gamma-ray Space TelescopeGalactic haloSpectral densityAnisotropy

Abstract

fetched live from OpenAlex

Abstract Many-degree-scale gamma-ray halos are expected to surround extragalactic high-energy gamma-ray sources. These arise from the inverse Compton emission of an intergalactic population of relativistic electron/positron pairs generated by the annihilation of gamma rays on the extragalactic background light. These are typically anisotropic due to the jetted structure from which they originate (in the case of radio galaxies) or are oriented perpendicular to a large-scale intergalactic magnetic field (for blazar geometries). Here, we propose a novel method for detecting these inverse Compton gamma-ray halos based on this anisotropic structure that is centered on the active galactic nucleus (AGN). By marginalizing over the radial distribution of halo photons, we demonstrate that the angular power spectrum shows a characteristic sawtooth pattern with a dominant dipolar power and elevated even multipoles. Specifically, we show that by stacking those angular power spectra instead of images, it is possible to robustly detect gamma-ray halos with existing Fermi Large Area Telescope (LAT) observations for a broad class of intergalactic magnetic fields. Importantly, by testing a large number of systematics, our suggested power spectrum statistics appears to be robust and unbiased with respect to systematic uncertainties within the LAT instrumental response and associated with contaminating astronomical 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.033
GPT teacher head0.298
Teacher spread0.265 · 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 designTheoretical or conceptual
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

Citations15
Published2017
Admission routes1
Has abstractyes

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