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Record W6929094261 · doi:10.48550/arxiv.2002.11178

Limits on Absorption from a 332-MHz survey for Fast Radio Bursts

2020· preprint· en· W6929094261 on OpenAlexaboutno aff

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsAbsorption (acoustics)Compton scatteringRadio telescopeSupernovaObservatorySkyEjectaTelescope

Abstract

fetched live from OpenAlex

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 has 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 a FRB survey recently conducted at the Jodrell Bank Observatory at 332 MHz with the 76-m Lovell telescope for a total of 58 days. We did not detect any FRBs in the survey and report a 90$\%$ 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 Super-Luminous SuperNovae (SLSNe).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.430
GPT teacher head0.294
Teacher spread0.137 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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