Limits on absorption from a 332-MHz survey for fast radio bursts
Bibliographic record
Abstract
ABSTRACT Fast radio bursts (FRBs) are bright, extragalactic radio pulses whose origins are still unknown. Until recently, most FRBs have been detected at frequencies greater than 1 GHz with a few exceptions at 800 MHz. The recent discoveries of FRBs at 400 MHz from the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope have opened up possibilities for new insights about the progenitors while many other low-frequency surveys in the past have failed to find any FRBs. Here, we present results from an FRB survey recently conducted at the Jodrell Bank Observatory at 332 MHz with the 76-m Lovell telescope for a total of 58 d. We did not detect any FRBs in the survey and report a 90${{\ \rm per\ cent}}$ upper limit of 5500 FRBs per day per sky for a Euclidean Universe above a fluence threshold of 46 Jy ms. We discuss the possibility of absorption as the main cause of non-detections in low-frequency (<800 MHz) searches and invoke different absorption models to explain the same. We find that Induced Compton Scattering alone cannot account for absorption of radio emission and that our simulations favour a combination of Induced Compton Scattering and Free-Free Absorption to explain the non-detections. For a free–free absorption scenario, our constraints on the electron density are consistent with those expected in the post-shock region of the ionized ejecta in superluminous supernovae.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".