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Record W2803297960 · doi:10.22323/1.301.0557

Detailed simulations of Fermi-LAT constraints on UHECR production scenarios

2017· article· en· W2803297960 on OpenAlexfundno aff
Marco Stein Muzio, Glennys R. Farrar, M. Unger

Bibliographic record

VenueProceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersYork UniversityNational Science Foundation
KeywordsFermi Gamma-ray Space TelescopePhysicsPhotodisintegrationFlux (metallurgy)UniversePosition (finance)AstrophysicsPhotonQuantum mechanics

Abstract

fetched live from OpenAlex

We have performed high-resolution simulations of the flux and spectrum of gammas detectable by Fermi-LAT, coming from UHECR-induced cascades, using the state-of-the-art (Gilmore) model of extragalactic background light, ELMAG to simulate the EM cascade (modified to work correctly for the Gilmore EBL) and CRPropa3 to simulate the production of EM secondaries from UHECR propagation. We examine the validity and limitations of the Liu et al. (2016) constraints, which found that we live in a local UHECR over-density. We also examine whether a model such as that of Unger, Farrar, and Anchordoqui (2015) -- which explains the full extragalactic CR spectrum and composition including the ankle, based on nuclear photodisintegration in the neighborhood surrounding the source -- satisfies the Fermi-LAT constraints, or requires Earth to be in an atypical position in the Universe as claimed by Liu et al. for pure proton primaries. We conclude that a mixed-composition scenario, like that of Unger, Farrar, and Anchordoqui, satisfies Fermi-LAT constraints evading any need for Earth to be in an atypical position in the Universe.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.281
Teacher spread0.251 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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