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Record W4311113967 · doi:10.3847/1538-3881/ac9eb8

Radio-loud Exoplanet-exomoon Survey: GMRT Search for Electron Cyclotron Maser Emission

2022· article· en· W4311113967 on OpenAlexaff
Mayank Narang, A. Oza, Kaustubh Hakim, P. Manoj, Ravinder K. Banyal, Daniel Thorngren

Bibliographic record

VenueThe Astronomical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersTata Institute of Fundamental ResearchDepartment of Atomic Energy, Government of IndiaCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsPhysicsExoplanetGiant Metrewave Radio TelescopeAstrophysicsAstronomyFlux (metallurgy)JovianStarsPlanetGalaxyRadio galaxy

Abstract

fetched live from OpenAlex

Abstract We conducted the first dedicated search for signatures of exoplanet–exomoon interactions using the Giant Metrewave Radio Telescope (GMRT) as part of the radio-loud exoplanet-exomoon survey. Due to stellar tidal heating, irradiation, and subsequent atmospheric escape, candidate “exo-Io” systems are expected to emit up to 10 6 times more plasma flux than the Jupiter-Io DC circuit. This can induce detectable radio emission from the exoplanet-exomoon system. We analyze three “exo-Io” candidate stars: WASP-49, HAT-P 12, and HD 189733. We perform 12 hr phase-curve observations of WASP-49b at 400 MHz during primary & secondary transit, as well as first & third quadratures achieving a 3 σ upper limit of 0.18 mJy beam −1 averaged over four days. HAT-P 12 was observed with GMRT at 150 and 325 MHz. We further analyzed the archival data of HD 189733 at 325 MHz. No emission was detected from the three systems. However, we place strong upper limits on radio flux density. Given that most exo-Io candidates orbit hot Saturns, we encourage more multiwavelength searches (in particular low frequencies) to span the lower range of exoplanet B-field strengths constrained here.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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 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

Citations23
Published2022
Admission routes1
Has abstractyes

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