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Record W2970416319 · doi:10.3847/2041-8213/ab5b08

Faint Repetitions from a Bright Fast Radio Burst Source

2019· article· en· W2970416319 on OpenAlexaff
Pravir Kumar, R. M. Shannon, S. Osłowski, Hao Qiu, Shivani Bhandari, Wael Farah, Chris Flynn, M. Kerr, D. R. Lorimer, Jean‐Pierre Macquart, Cherry Ng, Chris Phillips, Danny C. Price, R. Spiewak

Bibliographic record

VenueThe Astrophysical Journal Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsPhysicsAstrophysicsFast radio burstTelescopeRadio telescopePulse (music)Source countsGreen Bank TelescopeSpectral lineAstronomyGalaxyOpticsRedshiftDetector

Abstract

fetched live from OpenAlex

Abstract We report the detection of repeat bursts from the source of FRB 171019, one of the brightest fast radio bursts (FRBs) detected in the Australian Square Kilometre Array Pathfinder (ASKAP) fly’s eye survey. Two bursts from the source were detected with the Green Bank Telescope in observations centered at 820 MHz. The repetitions are a factor of ∼590 fainter than the ASKAP-discovered burst. All three bursts from this source show no evidence of scattering and have consistent pulse widths. The pulse spectra show modulation that could be evidence for either steep spectra or patchy emission. The two repetitions were the only ones found in an observing campaign for this FRB totaling 1000 hr, which also included ASKAP and the 64 m Parkes radio telescope, over a range of frequencies (720–2000 MHz) at epochs spanning two years. The inferred scaling of repetition rate with fluence of this source agrees with the other repeating source, FRB 121102. The detection of faint pulses from FRB 171019 shows that at least some FRBs selected from bright samples will repeat if follow-up observations are conducted with more sensitive telescopes.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.253
Teacher spread0.247 · 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

Citations121
Published2019
Admission routes1
Has abstractyes

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