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Record W7161778687 · doi:10.82308/15541

A high time resolution search for gravitationally lensed fast radio bursts using the CHIME telescope

2022· dissertation· en· W7161778687 on OpenAlexaboutno aff
Zarif Kader

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
Fundersnot available
KeywordsGravitational lensStrong gravitational lensingTelescopeWeak gravitational lensingGravitational waveRadio telescopeWaveformSIGNAL (programming language)

Abstract

fetched live from OpenAlex

The gravitational field of objects such as primordial black holes should create an image pair of the waveform of a fast radio burst (FRB), where one image is delayed in its arrival time and has a smaller flux magnification than the other image. We search for a copy of FRB waveforms in observations from the Canadian Hydrogen Mapping Intensity Experiment (CHIME) by performing a time-lag auto-correlation with a matched filter. A gravitational lensing signal in the time-lag domain will appear as a statistically significant excursion. We model the statistics and test statistical significance with the use of the off-pulse telescope data. Using the CHIME telescope, we search for a lensing signature using data from 142 FRB events at 1.25 ns resolution. This method allows us to search for gravitational lensing signatures from 0.01 to 100 solar mass objects. We find no significant detections of a gravitational lensing signature. However, we do find evidence for correlations induced from plasma lensing structure for a subset of bursts

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 score0.127
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.018
GPT teacher head0.343
Teacher spread0.324 · 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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