On the relationship between the duration and energy of non-repeating fast radio bursts: census with the CHIME data
Bibliographic record
Abstract
ABSTRACT A correlation between the intrinsic energy and the burst duration of non-repeating fast radio bursts (FRBs) has been reported. If it exists, the correlation could be used to estimate the intrinsic energy from the duration, and thus could provide a new distance measure for cosmology. However, the correlation arose from small-number statistics (68 FRBs) and was not free from contamination by latent repeating populations, which might not have such a correlation. Finding a way to separate/exclude the repeating bursts from the mixture of all different types of FRBs is essential for investigating this property. Using a much larger sample from the new FRB catalogue (containing 536 FRBs) recently released by the CHIME (Canadian Hydrogen Intensity Mapping Experiment)/FRB Project, combined with a new classification method developed based on unsupervised machine learning, we carried out further scrutiny of the relationship. We found that there is a weak correlation between the intrinsic energy and duration for non-repeating FRBs at z < 0.3, with a Kendall τ correlation coefficient of 0.239 and a significance of 0.001 (statistically significant), whose slope looks similar to that of gamma-ray bursts. This correlation becomes weaker and insignificant at higher redshifts (z > 0.3), possibly owing to the lack of faint FRBs at high z and/or the redshift evolution of the correlation. The ‘scattering time’ in the CHIME/FRB catalogue shows an intriguing trend: it varies along the line obtained from a linear fit on the energy versus duration plane between these two parameters. A possible cosmological application of the relationship must wait for more observations of faint FRBs at high z.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".