Impact of propagation effects on the spectro-temporal properties of Fast Radio Bursts
Bibliographic record
Abstract
We present a mathematical analysis of the spectro-temporal properties of Fast Radio Bursts (FRBs), focusing on the distortions introduced by propagation effects such as scattering and inaccurate dedispersion.By examining the impact of different scattering timescales and residual dispersion measures (DMs), both independently and in combination, we identify systematic trends in the sub-burst slope law as defined within the framework of the Triggered Relativistic Dynamical Model (TRDM).These effects primarily alter the measurements of the sub-burst slope and duration, thereby also modifying their correlations with other properties, such as central frequency and bandwidth.Our results show that scatter-induced temporal broadening affects duration more than slope, with weak to moderate scattering subtly modifying the sub-burst slope law and strong scattering causing significant deviations.Residual dispersion preferentially modifies the slope, further changing the trends predicted by the sub-burst slope law.Ultra-short bursts (or ultra-FRBs) emerge as particularly susceptible to these effects even at relatively high frequencies, underscoring the need for precise treatment of scattering and accurate dedispersion before performing analyses.Our findings emphasize the necessity for higher frequency observations (especially for ultra-FRBs) to improve the DM estimates as well as the measurements of spectro-temporal properties.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".