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Record W4318815871 · doi:10.3847/1538-4357/aca899

Study of Radio Transients from the Quiet Sun during an Extremely Quiet Time

2023· article· en· W4318815871 on OpenAlexaff
Surajit Mondal, Divya Oberoi, Ayan Biswas

Bibliographic record

VenueThe Astrophysical Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersDepartment of Atomic Energy, Government of IndiaCurtin University of TechnologyAstronomy Australia LimitedNational Cancer Research InstituteCommonwealth Scientific and Industrial Research OrganisationGovernment of Western AustraliaNational Science Foundation
KeywordsQUIETPhysicsAstronomyAstrophysics

Abstract

fetched live from OpenAlex

Abstract In this work, we study a class of recently discovered meter-wave solar transients referred to as Weak Impulsive Narrowband Quiet Sun Emission (WINQSEs). Their strength is a few percent of the quiet Sun background and is characterized by their very impulsive, narrowband, and ubiquitous presence in quiet Sun regions. Mondal et al. (2020) hypothesized that these emissions might be the radio counterparts of nanoflares, and their potential significance warrants detailed studies. Here we present an analysis of data from an extremely quiet time and with improved methodology over the previous work. As before, we detect numerous WINQSEs, which we have used for their further characterization. Their key properties, namely, their impulsive nature and ubiquitous presence in the quiet Sun, are observed in these data as well. Interestingly, we also find some of the observed properties to differ significantly from the earlier work. With this demonstration of routine detection of WINQSEs, we hope to engender interest in the larger community to build a deeper understanding of WINQSEs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.013
GPT teacher head0.238
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2023
Admission routes1
Has abstractyes

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