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Record W4298330163 · doi:10.26443/msurj.v13i1.25

Exploration of Fermi-LAT Data: An Analysis of Pulsar J1930+1852

2018· article· en· W4298330163 on OpenAlexafffund
Amanda M. Cook

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsPulsarPhysicsPulsar wind nebulaFermi Gamma-ray Space TelescopeAstrophysicsNebulaAstronomyPoint sourceMillisecond pulsarPulsar planetBinary pulsarStarsQuantum mechanics

Abstract

fetched live from OpenAlex

Background: Fermi-LAT’s 9-year data set of astrophysical gamma-rays (recently reprocessed) has revealed many new astrophysical sources. A closer analysis of one of these previously unseen sources, PSR J1930+1852 and associated pulsar wind nebula, G54.0+0.3, could help to confirm the gamma-ray emission mechanism of pulsars. Methods: An investigation and analysis of PSR J1930+1852 and PWN G54.0+0.3 using Fermi-LAT data and science tools using maximum likelihood fitting is detailed.Results: A 4.3 σ (p = 0.000017) excess above background was observed at the coordinates of the pulsar/pulsar wind nebula and the sources spectrum appears to be consistent with a single power law. Limitations: The sources in the models are modelled as point sources. Further studies may want to consider the possibility of extended sources in the modelled region. Conclusion: There is evidence for a Fermi-LAT detection of this pulsar wind nebula and the source spectrum appears to be consistent with a standard power law. An upper limit calculation predicts only about 100 events with energy above 1 GeV in the 9-year data set so a pulsation search was not conducted.

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)0.001
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.182
GPT teacher head0.423
Teacher spread0.242 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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