MétaCan
Menu
Back to cohort
Record W2283667829 · doi:10.1093/mnras/stw907

Constraints on the FRB rate at 700–900 MHz

2016· article· en· W2283667829 on OpenAlexaff
Liam Connor, Hsiu-Hsien Lin, Kiyoshi W. Masui, Niels Oppermann, Ue‐Li Pen, J. B. Peterson, Alexander Roman, Jonathan Sievers

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British ColumbiaCanadian Institute for Theoretical AstrophysicsPerimeter InstituteUniversity of Toronto
Fundersnot available
KeywordsPhysicsSkyFast radio burstAstrophysicsSigmaScalingFlux (metallurgy)Consistency (knowledge bases)Log-normal distributionAstronomyStatisticsDiscrete mathematics

Abstract

fetched live from OpenAlex

Estimating the all-sky rate of fast radio bursts (FRBs) has been difficult due to small-number statistics and the fact that they are seen by disparate surveys in different regions of the sky. In this paper we provide limits for the FRB rate at 800 MHz based on the only burst detected at frequencies below 1.4 GHz, FRB 110523. We discuss the difficulties in rate estimation, particularly in providing an all-sky rate above a single fluence threshold. We find an implied rate between 700 and 900 MHz that is consistent with the rate at 1.4 GHz, scaling to |$6.4^{+29.5}_{-5.0} \times 10^3$| sky−1 d−1 for an HTRU-like survey. This is promising for upcoming experiments below a GHz like CHIME and UTMOST, for which we forecast detection rates. Given 110523's discovery at 32σ with nothing weaker detected, down to the threshold of 8σ, we find consistency with a Euclidean flux distribution but disfavour steep distributions, ruling out γ > 2.2.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.012
GPT teacher head0.259
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

Citations42
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical SocietySame topicPulsars and Gravitational Waves ResearchFrench-language works237,207