Measurements and Scripts for "Spectro-temporal analysis of ultra-fast radio bursts using per-channel arrival times" paper
Bibliographic record
Abstract
This repository contains the necessary files to reproduce the measurements and figures published in "Spectro-temporal analysis of ultra-fast radio bursts using per-channel arrival times". This includes measurement spreadsheets, measurement figures, analysis scripts, and figure scripts. Scripts are written in Python. Burst files containing the FRB waterfalls in .npz format can be arranged through correspondence. Each PDF file named after an FRB source contains the waterfall plots of each burst with their measurements overlaid. The spreadsheets included are: allmeasurements_postfilter.csv Spreadsheet of the 433 measurements remaining after measurement filters are applied. These measurements are used in the figures and form the basis of the analysis and conclusions. allmeasurements_prefilter.csv Sheet of all measurements before filtering is applied. alldrifts.csv Sheet of multi-component driftrates measured using the arrival times method. alldrifts_acf.csv Sheet of multi-component driftrates measured using the ACF Gaussian method. burstdm_allmeasurements.csv Spreadsheet of measurements taken at each burst's individually determined DM. channelduration_allmeasurements.csv Measurements with duration defined as the average of channel durations. dmoptimized_allmeasurements.csv Measurements taken at each source's slope law corrected, 'optimized' DM. fluxdensities.csv Burst flux densities used in Figure 5. The scripts included are: arrivaltimes.py The implementation of the arrival times measurement pipeline described in the paper. Please note that this script is packaged with (and depends on) frbgui, which is available on pip and Github. Documentation on using arrivaltimes.py is available here. measurebursts.py This script performs the measurement of each burst. For each burst, the measurement options passed to arrivaltimes.py are listed. fig_spectraplots.py This script produces Figures 3 to 6, D1, D2, and D3 of the paper. Please email If you would like the code and measurements for the method comparison figures (Figs 7 and 8). measurement_example.py Shows the basic structure of measurebursts.py and can serve as a template for using the arrivaltimes.py pipeline.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.361 | 0.263 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".