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Record W3120223812 · doi:10.3847/2041-8213/abf11e

The Imprint of Large-scale Structure on the Ultrahigh-energy Cosmic-Ray Sky

2021· article· en· W3120223812 on OpenAlexfundno aff
C. Ding, Noémie Globus, Glennys R. Farrar

Bibliographic record

VenueThe Astrophysical Journal Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersYork UniversityNational Science Foundation
KeywordsPhysicsCosmic rayAstrophysicsAnisotropyUltra-high-energy cosmic rayDipolePierre Auger ObservatoryMagnetic fieldSkyAstronomyComputational physics

Abstract

fetched live from OpenAlex

Abstract Ultrahigh-energy cosmic rays (UHECRs) are atomic nuclei from space with vastly higher energies than any other particles ever observed. Their origin and chemical composition remain a mystery. As we show here, the large and intermediate angular scale anisotropies observed by the Pierre Auger Observatory are a powerful tool for understanding the origin of UHECRs. Without specifying any particular production mechanism but only postulating that the source distribution follows the matter distribution of the local universe, a good accounting of the magnitude, direction, and energy dependence of the dipole anisotropy at energies above 8 × 10 18 eV is obtained after taking into account the impact of energy losses during propagation (the “GZK horizon”), diffusion in the extragalactic magnetic field, and deflections in the Galactic magnetic field (GMF). This is a major step toward the long-standing hope of using UHECR anisotropies to constrain UHECR composition and magnetic fields. The observed dipole anisotropy is incompatible with a pure proton composition in this scenario. With a more accurate treatment of energy losses, it should be possible to further constrain the cosmic-ray composition and properties of the extragalactic magnetic field, self-consistently improve the GMF model, and potentially expose individual UHECR 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.700

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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 designBench or experimental
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

Citations48
Published2021
Admission routes1
Has abstractyes

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