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Record W7161812599 · doi:10.82308/55123

The Canadian hydrogen intensity mapping experiment fast radio burst project: Monitoring the interference environment and studying the bursting behaviour of SGR 1935+2154

2022· dissertation· en· W7161812599 on OpenAlexaboutno aff
Alice Curtin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
Fundersnot available
KeywordsInterference (communication)Radio programRadio spectrumNoise (video)

Abstract

fetched live from OpenAlex

Les sursauts radio rapides (FRB pour fast radio bursts en anglais) sont des émissions radio hautement énergétiques, d'une durée de quelques millisecondes, d'origines extragalactiques. Quand combinés avec leur position dans le ciel inconnue, leur nature transoire et leur origine les rendent difficiles à découvrir et requièrent de nouveaux types de télescopes et de nouveaux pipelines de détection. Dans cette thèse, nous allons nous concentrer sur un de ces télescopes, le Canadian Hydrogen Intensity Mapping Experiment (CHIME) et son projet de FRB. Depuis sa mise en fonction, CHIME/FRB a détecté plus de deux mille FRBs, des ordres de grandeur de plus que tout autre télescope. Ici, je démontre un nouveau pipeline pour caractériser l'interférence de fréquence radio au CHIME/FRB. Je présente la détermination d'une limite supérieure des émissions radio produites par le magnétar SGR 1935+2154, connu pour produire des des sursauts radio semblables à des FRBs. Pour sept différents sursauts rayon X de haute énergie provenant du SGR 1935+2154, j'ai limité les émissions radio simultanées à moins de quelques kJy. J'ai aussi découvert que les émissions ressemblant à des FRBs produites par SGR 1935+2154, sont distinctes des autres sursauts typiques provenant de magnétars par leur propriétés spectrales et leur rapport radio/rayon X

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)0.000
metaresearch head score (Gemma)0.000
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 score0.181
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.320
Teacher spread0.288 · 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
Published2022
Admission routes1
Has abstractyes

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