MétaCan
Menu
Back to cohort
Record W6906669307 · doi:10.17605/osf.io/p5rtq

First Detection of Sub-Second Temporal Periodicities in Fast Radio Bursts: Advanced Signal Processing Reveals Systematic Structure in CHIME Data

2025· article· en· W6906669307 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPattern recognition (psychology)Signal processingStatistical signal processingCoherence (philosophical gambling strategy)WaveletRaw dataStatistical powerWavelet transformStatistical model

Abstract

fetched live from OpenAlex

This project presents the final submitted version of a peer-reviewed manuscript analyzing Fast Radio Burst (FRB) data from the Canadian Hydrogen Intensity Mapping Experiment (CHIME) for the period 2018–2019. The study applies a tri-phase decomposition approach across 50 individual FRBs to investigate the presence of stable temporal periodicities and harmonic structures. Using a combination of Hilbert transforms, FFTs, wavelet analyses, and statistical bootstrapping, the results reveal a class of bursts with persistent multi-peak structures and significant coherence across time-frequency domains. Appendix A includes detailed figure panels for each FRB analyzed, and a population-level statistical summary is provided at the end of the document. The study emphasizes methodological rigor and falsifiability, with all detections statistically validated against null controls and randomized benchmarks. A full Colab notebook with raw data extraction and processing code is available upon request.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0630.005

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueOSF Preprints (OSF Preprints)Same topicEarthquake Detection and AnalysisFrench-language works237,207