Detecting fast radio bursts at decametric wavelengths
Bibliographic record
Abstract
Fast radio bursts (FRBs) are highly dispersed, sporadic radio pulses that are likely extragalactic in nature. Here we investigate the constraints on the source population from surveys carried out at frequencies $<1$~GHz. All but one FRB has so far been discovered in the 1--2~GHz band, but new and emerging instruments look set to become valuable probes of the FRB population at sub-GHz frequencies in the near future. In this paper, we consider the impacts of free-free absorption and multi-path scattering in our analysis via a number of different assumptions about the intervening medium. We consider previous low frequency surveys alongwith an ongoing survey with the University of Technology digital backend for the Molonglo Observatory Synthesis Telescope (UTMOST) as well as future observations with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and the Hydrogen Intensity and Real-Time Analysis Experiment (HIRAX). We predict that CHIME and HIRAX will be able to observe $\sim$ 30 or more FRBs per day, even in the most extreme scenarios where free-free absorption and scattering can significantly impact the fluxes below 1~GHz. We also show that UTMOST will detect 1--2 FRBs per month of observations. For CHIME and HIRAX, the detection rates also depend greatly on the assumed FRB distance scale. Some of the models we investigated predict an increase in the FRB flux as a function of redshift at low frequencies. If FRBs are truly cosmological sources, this effect may impact future surveys in this band, particularly if the FRB population traces the cosmic star formation rate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".